Point cloud data transmission device, point cloud data transmission method, point cloud data reception device, and point cloud data reception method

ABSTRACT

Disclosed herein is a method of transmitting point cloud data. The method may include acquiring point cloud data, encoding geometry information including positions of points of the point cloud data, generating one or more levels of detail (LODs) based on the geometry information and selecting one or more neighbor points of each point to be attribute-encoded based on the one or more LODs, encoding attribute information of each point based on the selected one or more neighbor points of each point, and transmitting the encoded geometry information, the encoded attribute information, and signaling information.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/143,793, filed on Jan. 7, 2021, which claims the benefit of U.S.Provisional Application No. 62/958,264, filed on Jan. 7, 2020, andKorean Patent Application No. 10-2020-0005544, filed on Jan. 15, 2020.The disclosures of the prior applications are incorporated by referencein their entirety.

TECHNICAL FIELD

Embodiments relate to a method and apparatus for processing point cloudcontent.

BACKGROUND

Point cloud content is content represented by a point cloud, which is aset of points belonging to a coordinate system representing athree-dimensional space. The point cloud content may express mediaconfigured in three dimensions, and is used to provide various servicessuch as virtual reality (VR), augmented reality (AR), mixed reality(MR), XR (Extended Reality), and self-driving services. However, tens ofthousands to hundreds of thousands of point data are required torepresent point cloud content. Therefore, there is a need for a methodfor efficiently processing a large amount of point data.

SUMMARY

An object of the present disclosure devised to solve the above-describedproblems is to provide a point cloud data transmission device, a pointcloud data transmission method, a point cloud data reception device, anda point cloud data reception method for efficiently transmitting andreceiving a point cloud

Another object of the present disclosure is to provide a point clouddata transmission device, a point cloud data transmission method, apoint cloud data reception device, and a point cloud data receptionmethod for addressing latency and encoding/decoding complexity

Another object of the present disclosure is to provide a point clouddata transmission device, a point cloud data transmission method, apoint cloud data reception device, and a point cloud data receptionmethod for improving the compression performance of the point cloud byimproving the technique of encoding attribute information ofgeometry-based point cloud compression (G-PCC).

Another object of the present disclosure is to provide a point clouddata transmission device, a point cloud data transmission method, apoint cloud data reception device, and a point cloud data receptionmethod for enhancing compression efficiency while supporting parallelprocessing of attribute information of G-PCC.

Another object of the present disclosure is to provide a point clouddata transmission apparatus, a point cloud data transmission method, apoint cloud data reception apparatus, and a point cloud data receptionmethod for reducing the size of an attribute bitstream and enhancingcompression efficiency of attributes by selecting neighbor points usedfor attribute prediction in consideration of an attribute correlationbetween points of content when attribute information of G-PCC isencoded.

Another object of the present disclosure is to provide a point clouddata transmission apparatus, a point cloud data transmission method, apoint cloud data reception apparatus, and a point cloud data receptionmethod for reducing the size of an attribute bitstream and enhancingcompression efficiency of attributes by applying a maximum neighborpoint distance and selecting neighbor points used for attributeprediction when attribute information of G-PCC is encoded.

Objects of the present disclosure are not limited to the aforementionedobjects, and other objects of the present disclosure which are notmentioned above will become apparent to those having ordinary skill inthe art upon examination of the following description.

To achieve these objects and other advantages and in accordance with thepurpose of the disclosure, as embodied and broadly described herein, apoint cloud data transmission method may include acquiring point clouddata, encoding geometry information including positions of points of thepoint cloud data, generating one or more levels of detail (LODs) basedon the geometry information and selecting one or more neighbor points ofeach point to be attribute-encoded based on the one or more LODs,encoding attribute information of each point based on the selected oneor more neighbor points of each point, and transmitting the encodedgeometry information, the encoded attribute information, and signalinginformation.

According to embodiments, the selected one or more neighbor points ofeach point may be located within a maximum neighbor point distance.

According to embodiments, the maximum neighbor point distance may bedetermined based on a base neighbor point distance and a maximumneighbor point range.

According to embodiments, when the one or more LODs are generated basedon an octree, the base neighbor point distance may be determined basedon a diagonal distance of one node at a specific LOD.

According to embodiments, the maximum neighbor point range may be thenumber of neighbor nodes of each point and may be signaled in thesignaling information.

A point cloud data transmission apparatus according to embodiments mayinclude an acquisition unit configured to acquire point cloud data, ageometry encoder configured to encode geometry information includingpositions of points of the point cloud data, an attribute encoderconfigured to generate one or more levels of detail (LODs) based on thegeometry information, select one or more neighbor points of each pointto be attribute-encoded based on the one or more LODs, and encodeattribute information of each point based on the selected one or moreneighbor points of each point, and a transmitter configured to transmitthe encoded geometry information, the encoded attribute information, andsignaling information.

According to embodiments, the selected one or more neighbor points ofeach point may be located within a maximum neighbor point distance.

According to embodiments, the maximum neighbor point distance may bedetermined based on a base neighbor point distance and a maximumneighbor point range.

According to embodiments, when the one or more LODs are generated basedon an octree, the base neighbor point distance may be determined basedon a diagonal distance of one node at a specific LOD.

According to embodiments, the maximum neighbor point range may be thenumber of neighbor nodes of each point and may be signaled in thesignaling information.

A point cloud data reception apparatus according to embodiments mayinclude a receiver configured to receive geometry information, attributeinformation, and signaling information, a geometry decoder configured todecode the geometry information based on the signaling information, anattribute decoder configured to generate one or more levels of detail(LODs) based on the geometry information, select one or more neighborpoints of each point to be attribute-decoded based on the one or moreLODs, and decode attribute information of each point based on theselected one or more neighbor points of each point and the signalinginformation, and a renderer configured to render restored point clouddata based on the decoded geometry information and the decoded attributeinformation.

According to embodiments, the selected one or more neighbor points ofeach point may be located within a maximum neighbor point distance.

According to embodiments, the maximum neighbor point distance may bedetermined based on a base neighbor point distance and a maximumneighbor point range.

According to embodiments, when the one or more LODs are generated basedon an octree, the base neighbor point distance may be determined basedon a diagonal distance of one node at a specific LOD.

According to embodiments, the maximum neighbor point range may be thenumber of neighbor nodes of each point and may be signaled in thesignaling information.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the disclosure and are incorporated in and constitute apart of this application, illustrate embodiment(s) of the disclosure andtogether with the description serve to explain the principle of thedisclosure. In the drawings:

FIG. 1 illustrates an exemplary point cloud content providing systemaccording to embodiments.

FIG. 2 is a block diagram illustrating a point cloud content providingoperation according to embodiments.

FIG. 3 illustrates an exemplary process of capturing a point cloud videoaccording to embodiments.

FIG. 4 illustrates an exemplary block diagram of point cloud videoencoder according to embodiments.

FIG. 5 illustrates an example of voxels in a 3D space according toembodiments.

FIG. 6 illustrates an example of octree and occupancy code according toembodiments.

FIG. 7 illustrates an example of a neighbor node pattern according toembodiments.

FIG. 8 illustrates an example of point configuration of a point cloudcontent for each LOD according to embodiments.

FIG. 9 illustrates an example of point configuration of a point cloudcontent for each LOD according to embodiments.

FIG. 10 illustrates an example of a block diagram of a point cloud videodecoder according to embodiments.

FIG. 11 illustrates an example of a point cloud video decoder accordingto embodiments.

FIG. 12 illustrates a configuration for point cloud video encoding of atransmission device according to embodiments.

FIG. 13 illustrates a configuration for point cloud video decoding of areception device according to embodiments.

FIG. 14 illustrates an exemplary structure operatively connectable witha method/device for transmitting and receiving point cloud dataaccording to embodiments.

FIG. 15 illustrates an example of a point cloud transmission deviceaccording to embodiments.

FIGS. 16A to 16C illustrate an embodiment of partitioning a bounding boxinto one or more tiles.

FIG. 17 illustrates an example of a geometry encoder and an attributeencoder according to embodiments.

FIG. 18 is a diagram illustrating an example of generating LODs based onan octree according to embodiments.

FIG. 19 is a diagram illustrating an example of arranging points of apoint cloud in a Morton code order according to embodiments.

FIG. 20 is a diagram illustrating an example of searching for neighborpoints based on LOD according to embodiments.

FIGS. 21A and 21B illustrate examples of point cloud contents accordingto embodiments.

FIGS. 22A and 22B illustrate examples of an average distance, a minimumdistance, and a maximum distance of each point belonging to neighborpoint set according to embodiments.

FIG. 23A illustrates an example of acquiring a diagonal distance of anoctree node of LOD0 according to embodiments.

FIG. 23B illustrates an example of acquiring a diagonal distance of anoctree node of LOD1 according to embodiments.

FIGS. 24A and 24B illustrate examples of a range that may be selected asa neighbor point at each LOD.

FIG. 25 illustrates examples of a base neighbor point distance belongingto each LOD according to embodiments.

FIG. 26 illustrates another example of searching for neighbor pointsbased on LOD according to embodiments.

FIG. 27 illustrates another example of searching for neighbor pointsbased on LOD according to embodiments.

FIG. 28 illustrates an example of a point cloud reception deviceaccording to embodiments.

FIG. 29 illustrates an example of a geometry decoder and an attributedecoder according to embodiments.

FIG. 30 illustrates an exemplary bitstream structure for point clouddata for transmission/reception according to embodiments.

FIG. 31 illustrates an exemplary bitstream structure for point clouddata according to embodiments.

FIG. 32 illustrates a connection relationship between components in abitstream of point cloud data according to embodiments.

FIG. 33 illustrates an embodiment of a syntax structure of a sequenceparameter set according to embodiments.

FIG. 34 illustrates an embodiment of a syntax structure of a geometryparameter set according to embodiments.

FIG. 35 illustrates an embodiment of a syntax structure of an attributeparameter set according to embodiments.

FIG. 36 illustrates another embodiment of a syntax structure of anattribute parameter set according to embodiments.

FIG. 37 illustrates an embodiment of a syntax structure of a tileparameter set according to embodiments.

FIG. 38 illustrates an embodiment of a syntax structure of geometryslice bitstream( ) according to embodiments.

FIG. 39 illustrates an embodiment of a syntax structure of geometryslice header according to embodiments.

FIG. 40 illustrates an embodiment of a syntax structure of geometryslice data according to embodiments.

FIG. 41 illustrates an embodiment of a syntax structure of attributeslice bitstream( ) according to embodiments.

FIG. 42 illustrates an embodiment of a syntax structure of attributeslice header according to embodiments.

FIG. 43 illustrates another embodiment of a syntax structure ofattribute slice header according to embodiments.

FIG. 44 illustrates an embodiment of a syntax structure of attributeslice data according to embodiments.

FIG. 45 is a flowchart of a method of transmitting point cloud dataaccording to embodiments.

FIG. 46 is a flowchart of a method of receiving point cloud dataaccording to embodiments.

DETAILED DESCRIPTION

Description will now be given in detail according to exemplaryembodiments disclosed herein, with reference to the accompanyingdrawings. For the sake of brief description with reference to thedrawings, the same or equivalent components may be provided with thesame reference numbers, and description thereof will not be repeated. Itshould be noted that the following examples are only for embodying thepresent disclosure and do not limit the scope of the present disclosure.What can be easily inferred by an expert in the technical field to whichthe present invention belongs from the detailed description and examplesof the present disclosure is to be interpreted as being within the scopeof the present disclosure.

The detailed description in this present specification should beconstrued in all aspects as illustrative and not restrictive. The scopeof the disclosure should be determined by the appended claims and theirlegal equivalents, and all changes coming within the meaning andequivalency range of the appended claims are intended to be embracedtherein.

Reference will now be made in detail to the preferred embodiments of thepresent disclosure, examples of which are illustrated in theaccompanying drawings. The detailed description, which will be givenbelow with reference to the accompanying drawings, is intended toexplain exemplary embodiments of the present disclosure, rather than toshow the only embodiments that can be implemented according to thepresent disclosure. The following detailed description includes specificdetails in order to provide a thorough understanding of the presentdisclosure. However, it will be apparent to those skilled in the artthat the present disclosure may be practiced without such specificdetails. Although most terms used in this specification have beenselected from general ones widely used in the art, some terms have beenarbitrarily selected by the applicant and their meanings are explainedin detail in the following description as needed. Thus, the presentdisclosure should be understood based upon the intended meanings of theterms rather than their simple names or meanings. In addition, thefollowing drawings and detailed description should not be construed asbeing limited to the specifically described embodiments, but should beconstrued as including equivalents or substitutes of the embodimentsdescribed in the drawings and detailed description.

FIG. 1 shows an exemplary point cloud content providing system accordingto embodiments.

The point cloud content providing system illustrated in FIG. 1 mayinclude a transmission device 10000 and a reception device 10004. Thetransmission device 10000 and the reception device 10004 are capable ofwired or wireless communication to transmit and receive point clouddata.

The point cloud data transmission device 10000 according to theembodiments may secure and process point cloud video (or point cloudcontent) and transmit the same. According to embodiments, thetransmission device 10000 may include a fixed station, a basetransceiver system (BTS), a network, an artificial intelligence (AI)device and/or system, a robot, an AR/VR/XR device and/or server.According to embodiments, the transmission device 10000 may include adevice, a robot, a vehicle, an AR/VR/XR device, a portable device, ahome appliance, an Internet of Thing (IoT) device, and an AIdevice/server which are configured to perform communication with a basestation and/or other wireless devices using a radio access technology(e.g., 5G New RAT (NR), Long Term Evolution (LTE)).

The transmission device 10000 according to the embodiments includes apoint cloud video acquisition unit 10001, a point cloud video encoder10002, and/or a transmitter (or communication module) 10003.

The point cloud video acquisition unit 10001 according to theembodiments acquires a point cloud video through a processing processsuch as capture, synthesis, or generation. The point cloud video ispoint cloud content represented by a point cloud, which is a set ofpoints positioned in a 3D space, and may be referred to as point cloudvideo data. The point cloud video according to the embodiments mayinclude one or more frames. One frame represents a still image/picture.Therefore, the point cloud video may include a point cloudimage/frame/picture, and may be referred to as a point cloud image,frame, or picture.

The point cloud video encoder 10002 according to the embodiments encodesthe acquired point cloud video data. The point cloud video encoder 10002may encode the point cloud video data based on point cloud compressioncoding. The point cloud compression coding according to the embodimentsmay include geometry-based point cloud compression (G-PCC) coding and/orvideo-based point cloud compression (V-PCC) coding or next-generationcoding. The point cloud compression coding according to the embodimentsis not limited to the above-described embodiment. The point cloud videoencoder 10002 may output a bitstream containing the encoded point cloudvideo data. The bitstream may contain not only the encoded point cloudvideo data, but also signaling information related to encoding of thepoint cloud video data.

The transmitter 10003 according to the embodiments transmits thebitstream containing the encoded point cloud video data. The bitstreamaccording to the embodiments is encapsulated in a file or segment (forexample, a streaming segment), and is transmitted over various networkssuch as a broadcasting network and/or a broadband network. Although notshown in the figure, the transmission device 10000 may include anencapsulator (or an encapsulation module) configured to perform anencapsulation operation. According to embodiments, the encapsulator maybe included in the transmitter 10003. According to embodiments, the fileor segment may be transmitted to the reception device 10004 over anetwork, or stored in a digital storage medium (e.g., USB, SD, CD, DVD,Blu-ray, HDD, SSD, etc.). The transmitter 10003 according to theembodiments is capable of wired/wireless communication with thereception device 10004 (or the receiver 10005) over a network of 4G, 5G,6G, etc. In addition, the transmitter may perform a necessary dataprocessing operation according to the network system (e.g., a 4G, 5G or6G communication network system). The transmission device 10000 maytransmit the encapsulated data in an on-demand manner.

The reception device 10004 according to the embodiments includes areceiver 10005, a point cloud video decoder 10006, and/or a renderer10007. According to embodiments, the reception device 10004 may includea device, a robot, a vehicle, an AR/VR/XR device, a portable device, ahome appliance, an Internet of Things (IoT) device, and an AIdevice/server which are configured to perform communication with a basestation and/or other wireless devices using a radio access technology(e.g., 5G New RAT (NR), Long Term Evolution (LTE)).

The receiver 10005 according to the embodiments receives the bitstreamcontaining the point cloud video data or the file/segment in which thebitstream is encapsulated from the network or storage medium. Thereceiver 10005 may perform necessary data processing according to thenetwork system (for example, a communication network system of 4G, 5G,6G, etc.). The receiver 10005 according to the embodiments maydecapsulate the received file/segment and output a bitstream. Accordingto embodiments, the receiver 10005 may include a decapsulator (or adecapsulation module) configured to perform a decapsulation operation.The decapsulator may be implemented as an element (or component ormodule) separate from the receiver 10005.

The point cloud video decoder 10006 decodes the bitstream containing thepoint cloud video data. The point cloud video decoder 10006 may decodethe point cloud video data according to the method by which the pointcloud video data is encoded (for example, in a reverse process of theoperation of the point cloud video encoder 10002). Accordingly, thepoint cloud video decoder 10006 may decode the point cloud video data byperforming point cloud decompression coding, which is the inverseprocess of the point cloud compression. The point cloud decompressioncoding includes G-PCC coding.

The renderer 10007 renders the decoded point cloud video data. Therenderer 10007 may output point cloud content by rendering not only thepoint cloud video data but also audio data. According to embodiments,the renderer 10007 may include a display configured to display the pointcloud content. According to embodiments, the display may be implementedas a separate device or component rather than being included in therenderer 10007.

The arrows indicated by dotted lines in the drawing represent atransmission path of feedback information acquired by the receptiondevice 10004. The feedback information is information for reflectinginteractivity with a user who consumes the point cloud content, andincludes information about the user (e.g., head orientation information,viewport information, and the like). In particular, when the point cloudcontent is content for a service (e.g., self-driving service, etc.) thatrequires interaction with the user, the feedback information may beprovided to the content transmitting side (e.g., the transmission device10000) and/or the service provider. According to embodiments, thefeedback information may be used in the reception device 10004 as wellas the transmission device 10000, or may not be provided.

The head orientation information according to embodiments is informationabout the user's head position, orientation, angle, motion, and thelike. The reception device 10004 according to the embodiments maycalculate the viewport information based on the head orientationinformation. The viewport information may be information about a regionof a point cloud video that the user is viewing. A viewpoint is a pointthrough which the user is viewing the point cloud video, and may referto a center point of the viewport region. That is, the viewport is aregion centered on the viewpoint, and the size and shape of the regionmay be determined by a field of view (FOV). Accordingly, the receptiondevice 10004 may extract the viewport information based on a vertical orhorizontal FOV supported by the device in addition to the headorientation information. Also, the reception device 10004 performs gazeanalysis or the like to check the way the user consumes a point cloud, aregion that the user gazes at in the point cloud video, a gaze time, andthe like. According to embodiments, the reception device 10004 maytransmit feedback information including the result of the gaze analysisto the transmission device 10000. The feedback information according tothe embodiments may be acquired in the rendering and/or display process.The feedback information according to the embodiments may be secured byone or more sensors included in the reception device 10004. According toembodiments, the feedback information may be secured by the renderer10007 or a separate external element (or device, component, or thelike).

The dotted lines in FIG. 1 represent a process of transmitting thefeedback information secured by the renderer 10007. The point cloudcontent providing system may process (encode/decode) point cloud databased on the feedback information. Accordingly, the point cloud videodecoder 10006 may perform a decoding operation based on the feedbackinformation. The reception device 10004 may transmit the feedbackinformation to the transmission device 10000. The transmission device10000 (or the point cloud video encoder 10002) may perform an encodingoperation based on the feedback information. Accordingly, the pointcloud content providing system may efficiently process necessary data(e.g., point cloud data corresponding to the user's head position) basedon the feedback information rather than processing (encoding/decoding)the entire point cloud data, and provide point cloud content to theuser.

According to embodiments, the transmission device 10000 may be called anencoder, a transmitting device, a transmitter, a transmission system, orthe like, and the reception device 10004 may be called a decoder, areceiving device, a receiver, a reception system, or the like.

The point cloud data processed in the point cloud content providingsystem of FIG. 1 according to embodiments (through a series of processesof acquisition/encoding/transmission/decoding/rendering) may be referredto as point cloud content data or point cloud video data. According toembodiments, the point cloud content data may be used as a conceptcovering metadata or signaling information related to the point clouddata.

The elements of the point cloud content providing system illustrated inFIG. 1 may be implemented by hardware, software, a processor, and/or acombination thereof.

FIG. 2 is a block diagram illustrating a point cloud content providingoperation according to embodiments.

The block diagram of FIG. 2 shows the operation of the point cloudcontent providing system described in FIG. 1 . As described above, thepoint cloud content providing system may process point cloud data basedon point cloud compression coding (e.g., G-PCC).

The point cloud content providing system according to the embodiments(for example, the point cloud transmission device 10000 or the pointcloud video acquisition unit 10001) may acquire a point cloud video(20000). The point cloud video is represented by a point cloud belongingto a coordinate system for expressing a 3D space. The point cloud videoaccording to the embodiments may include a Ply (Polygon File format orthe Stanford Triangle format) file. When the point cloud video has oneor more frames, the acquired point cloud video may include one or morePly files. The Ply files contain point cloud data, such as pointgeometry and/or attributes. The geometry includes positions of points.The position of each point may be represented by parameters (forexample, values of the X, Y, and Z axes) representing athree-dimensional coordinate system (e.g., a coordinate system composedof X, Y and Z axes). The attributes include attributes of points (e.g.,information about texture, color (in YCbCr or RGB), reflectance r,transparency, etc. of each point). A point has one or more attributes.For example, a point may have an attribute that is a color, or twoattributes that are color and reflectance.

According to embodiments, the geometry may be called positions, geometryinformation, geometry data, or the like, and the attribute may be calledattributes, attribute information, attribute data, or the like.

The point cloud content providing system (for example, the point cloudtransmission device 10000 or the point cloud video acquisition unit10001) may secure point cloud data from information (e.g., depthinformation, color information, etc.) related to the acquisition processof the point cloud video.

The point cloud content providing system (for example, the transmissiondevice 10000 or the point cloud video encoder 10002) according to theembodiments may encode the point cloud data (20001). The point cloudcontent providing system may encode the point cloud data based on pointcloud compression coding. As described above, the point cloud data mayinclude the geometry and attributes of a point. Accordingly, the pointcloud content providing system may perform geometry encoding of encodingthe geometry and output a geometry bitstream. The point cloud contentproviding system may perform attribute encoding of encoding attributesand output an attribute bitstream. According to embodiments, the pointcloud content providing system may perform the attribute encoding basedon the geometry encoding. The geometry bitstream and the attributebitstream according to the embodiments may be multiplexed and output asone bitstream. The bitstream according to the embodiments may furthercontain signaling information related to the geometry encoding andattribute encoding.

The point cloud content providing system (for example, the transmissiondevice 10000 or the transmitter 10003) according to the embodiments maytransmit the encoded point cloud data (20002). As illustrated in FIG. 1, the encoded point cloud data may be represented by a geometrybitstream and an attribute bitstream. In addition, the encoded pointcloud data may be transmitted in the form of a bitstream together withsignaling information related to encoding of the point cloud data (forexample, signaling information related to the geometry encoding and theattribute encoding). The point cloud content providing system mayencapsulate a bitstream that carries the encoded point cloud data andtransmit the same in the form of a file or segment.

The point cloud content providing system (for example, the receptiondevice 10004 or the receiver 10005) according to the embodiments mayreceive the bitstream containing the encoded point cloud data. Inaddition, the point cloud content providing system (for example, thereception device 10004 or the receiver 10005) may demultiplex thebitstream.

The point cloud content providing system (e.g., the reception device10004 or the point cloud video decoder 10005) may decode the encodedpoint cloud data (e.g., the geometry bitstream, the attribute bitstream)transmitted in the bitstream. The point cloud content providing system(for example, the reception device 10004 or the point cloud videodecoder 10005) may decode the point cloud video data based on thesignaling information related to encoding of the point cloud video datacontained in the bitstream. The point cloud content providing system(for example, the reception device 10004 or the point cloud videodecoder 10005) may decode the geometry bitstream to reconstruct thepositions (geometry) of points. The point cloud content providing systemmay reconstruct the attributes of the points by decoding the attributebitstream based on the reconstructed geometry. The point cloud contentproviding system (for example, the reception device 10004 or the pointcloud video decoder 10005) may reconstruct the point cloud video basedon the positions according to the reconstructed geometry and the decodedattributes.

The point cloud content providing system according to the embodiments(for example, the reception device 10004 or the renderer 10007) mayrender the decoded point cloud data (20004). The point cloud contentproviding system (for example, the reception device 10004 or therenderer 10007) may render the geometry and attributes decoded throughthe decoding process, using various rendering methods. Points in thepoint cloud content may be rendered to a vertex having a certainthickness, a cube having a specific minimum size centered on thecorresponding vertex position, or a circle centered on the correspondingvertex position. All or part of the rendered point cloud content isprovided to the user through a display (e.g., a VR/AR display, a generaldisplay, etc.).

The point cloud content providing system (for example, the receptiondevice 10004) according to the embodiments may secure feedbackinformation (20005). The point cloud content providing system may encodeand/or decode point cloud data based on the feedback information. Thefeedback information and the operation of the point cloud contentproviding system according to the embodiments are the same as thefeedback information and the operation described with reference to FIG.1 , and thus detailed description thereof is omitted.

FIG. 3 illustrates an exemplary process of capturing a point cloud videoaccording to embodiments.

FIG. 3 illustrates an exemplary point cloud video capture process of thepoint cloud content providing system described with reference to FIGS. 1to 2 .

Point cloud content includes a point cloud video (images and/or videos)representing an object and/or environment located in various 3D spaces(e.g., a 3D space representing a real environment, a 3D spacerepresenting a virtual environment, etc.). Accordingly, the point cloudcontent providing system according to the embodiments may capture apoint cloud video using one or more cameras (e.g., an infrared cameracapable of securing depth information, an RGB camera capable ofextracting color information corresponding to the depth information,etc.), a projector (e.g., an infrared pattern projector to secure depthinformation), a LiDAR, or the like. The point cloud content providingsystem according to the embodiments may extract the shape of geometrycomposed of points in a 3D space from the depth information and extractthe attributes of each point from the color information to secure pointcloud data. An image and/or video according to the embodiments may becaptured based on at least one of the inward-facing technique and theoutward-facing technique.

The left part of FIG. 3 illustrates the inward-facing technique. Theinward-facing technique refers to a technique of capturing images acentral object with one or more cameras (or camera sensors) positionedaround the central object. The inward-facing technique may be used togenerate point cloud content providing a 360-degree image of a keyobject to the user (e.g., VR/AR content providing a 360-degree image ofan object (e.g., a key object such as a character, player, object, oractor) to the user).

The right part of FIG. 3 illustrates the outward-facing technique. Theoutward-facing technique refers to a technique of capturing images anenvironment of a central object rather than the central object with oneor more cameras (or camera sensors) positioned around the centralobject. The outward-facing technique may be used to generate point cloudcontent for providing a surrounding environment that appears from theuser's point of view (e.g., content representing an external environmentthat may be provided to a user of a self-driving vehicle).

As shown in FIG. 3 , the point cloud content may be generated based onthe capturing operation of one or more cameras. In this case, thecoordinate system may differ among the cameras, and accordingly thepoint cloud content providing system may calibrate one or more camerasto set a global coordinate system before the capturing operation. Inaddition, the point cloud content providing system may generate pointcloud content by synthesizing an arbitrary image and/or video with animage and/or video captured by the above-described capture technique.The point cloud content providing system may not perform the capturingoperation described in FIG. 3 when it generates point cloud contentrepresenting a virtual space. The point cloud content providing systemaccording to the embodiments may perform post-processing on the capturedimage and/or video. In other words, the point cloud content providingsystem may remove an unwanted area (for example, a background),recognize a space to which the captured images and/or videos areconnected, and, when there is a spatial hole, perform an operation offilling the spatial hole.

The point cloud content providing system may generate one piece of pointcloud content by performing coordinate transformation on points of thepoint cloud video secured from each camera. The point cloud contentproviding system may perform coordinate transformation on the pointsbased on the coordinates of the position of each camera. Accordingly,the point cloud content providing system may generate contentrepresenting one wide range, or may generate point cloud content havinga high density of points.

FIG. 4 illustrates an exemplary point cloud video encoder according toembodiments.

FIG. 4 shows an example of the point cloud video encoder 10002 of FIG. 1. The point cloud video encoder reconstructs and encodes point clouddata (e.g., positions and/or attributes of the points) to adjust thequality of the point cloud content (to, for example, lossless, lossy, ornear-lossless) according to the network condition or applications. Whenthe overall size of the point cloud content is large (e.g., point cloudcontent of 60 Gbps is given for 30 fps), the point cloud contentproviding system may fail to stream the content in real time.Accordingly, the point cloud content providing system may reconstructthe point cloud content based on the maximum target bitrate to providethe same in accordance with the network environment or the like.

As described with reference to FIGS. 1 to 2 , the point cloud videoencoder may perform geometry encoding and attribute encoding. Thegeometry encoding is performed before the attribute encoding.

The point cloud video encoder according to the embodiments includes acoordinate transformer (Transform coordinates) 40000, a quantizer(Quantize and remove points (voxelize)) 40001, an octree analyzer(Analyze octree) 40002, and a surface approximation analyzer (Analyzesurface approximation) 40003, an arithmetic encoder (Arithmetic encode)40004, a geometric reconstructor (Reconstruct geometry) 40005, a colortransformer (Transform colors) 40006, an attribute transformer(Transform attributes) 40007, a RAHT transformer (RAHT) 40008, an LODgenerator (Generate LOD) 40009, a lifting transformer (Lifting) 40010, acoefficient quantizer (Quantize coefficients) 40011, and/or anarithmetic encoder (Arithmetic encode) 40012.

The coordinate transformer 40000, the quantizer 40001, the octreeanalyzer 40002, the surface approximation analyzer 40003, the arithmeticencoder 40004, and the geometry reconstructor 40005 may perform geometryencoding. The geometry encoding according to the embodiments may includeoctree geometry coding, direct coding, trisoup geometry encoding, andentropy encoding. The direct coding and trisoup geometry encoding areapplied selectively or in combination. The geometry encoding is notlimited to the above-described example.

As shown in the figure, the coordinate transformer 40000 according tothe embodiments receives positions and transforms the same intocoordinates. For example, the positions may be transformed into positioninformation in a three-dimensional space (for example, athree-dimensional space represented by an XYZ coordinate system). Theposition information in the three-dimensional space according to theembodiments may be referred to as geometry information.

The quantizer 40001 according to the embodiments quantizes the geometryinformation. For example, the quantizer 40001 may quantize the pointsbased on a minimum position value of all points (for example, a minimumvalue on each of the X, Y, and Z axes). The quantizer 40001 performs aquantization operation of multiplying the difference between the minimumposition value and the position value of each point by a presetquantization scale value and then finding the nearest integer value byrounding the value obtained through the multiplication. Thus, one ormore points may have the same quantized position (or position value).The quantizer 40001 according to the embodiments performs voxelizationbased on the quantized positions to reconstruct quantized points. Thevoxelization means a minimum unit representing position information in3D space. Points of point cloud content (or 3D point cloud video)according to the embodiments may be included in one or more voxels. Theterm voxel, which is a compound of volume and pixel, refers to a 3Dcubic space generated when a 3D space is divided into units (unit=1.0)based on the axes representing the 3D space (e.g., X-axis, Y-axis, andZ-axis). The quantizer 40001 may match groups of points in the 3D spacewith voxels. According to embodiments, one voxel may include only onepoint. According to embodiments, one voxel may include one or morepoints. In order to express one voxel as one point, the position of thecenter point of a voxel may be set based on the positions of one or morepoints included in the voxel. In this case, attributes of all positionsincluded in one voxel may be combined and assigned to the voxel.

The octree analyzer 40002 according to the embodiments performs octreegeometry coding (or octree coding) to present voxels in an octreestructure. The octree structure represents points matched with voxels,based on the octal tree structure.

The surface approximation analyzer 40003 according to the embodimentsmay analyze and approximate the octree. The octree analysis andapproximation according to the embodiments is a process of analyzing aregion containing a plurality of points to efficiently provide octreeand voxelization.

The arithmetic encoder 40004 according to the embodiments performsentropy encoding on the octree and/or the approximated octree. Forexample, the encoding scheme includes arithmetic encoding. As a resultof the encoding, a geometry bitstream is generated.

The color transformer 40006, the attribute transformer 40007, the RAHTtransformer 40008, the LOD generator 40009, the lifting transformer40010, the coefficient quantizer 40011, and/or the arithmetic encoder40012 perform attribute encoding. As described above, one point may haveone or more attributes. The attribute encoding according to theembodiments is equally applied to the attributes that one point has.However, when an attribute (e.g., color) includes one or more elements,attribute encoding is independently applied to each element. Theattribute encoding according to the embodiments includes color transformcoding, attribute transform coding, region adaptive hierarchicaltransform (RAHT) coding, interpolation-based hierarchicalnearest-neighbor prediction (prediction transform) coding, andinterpolation-based hierarchical nearest-neighbor prediction with anupdate/lifting step (lifting transform) coding. Depending on the pointcloud content, the RAHT coding, the prediction transform coding and thelifting transform coding described above may be selectively used, or acombination of one or more of the coding schemes may be used. Theattribute encoding according to the embodiments is not limited to theabove-described example.

The color transformer 40006 according to the embodiments performs colortransform coding of transforming color values (or textures) included inthe attributes. For example, the color transformer 40006 may transformthe format of color information (for example, from RGB to YCbCr). Theoperation of the color transformer 40006 according to embodiments may beoptionally applied according to the color values included in theattributes.

The geometry reconstructor 40005 according to the embodimentsreconstructs (decompresses) the octree and/or the approximated octree.The geometry reconstructor 40005 reconstructs the octree/voxels based onthe result of analyzing the distribution of points. The reconstructedoctree/voxels may be referred to as reconstructed geometry (restoredgeometry).

The attribute transformer 40007 according to the embodiments performsattribute transformation to transform the attributes based on thereconstructed geometry and/or the positions on which geometry encodingis not performed. As described above, since the attributes are dependenton the geometry, the attribute transformer 40007 may transform theattributes based on the reconstructed geometry information. For example,based on the position value of a point included in a voxel, theattribute transformer 40007 may transform the attribute of the point atthe position. As described above, when the position of the center of avoxel is set based on the positions of one or more points included inthe voxel, the attribute transformer 40007 transforms the attributes ofthe one or more points. When the trisoup geometry encoding is performed,the attribute transformer 40007 may transform the attributes based onthe trisoup geometry encoding.

The attribute transformer 40007 may perform the attribute transformationby calculating the average of attributes or attribute values ofneighboring points (e.g., color or reflectance of each point) within aspecific position/radius from the position (or position value) of thecenter of each voxel. The attribute transformer 40007 may apply a weightaccording to the distance from the center to each point in calculatingthe average. Accordingly, each voxel has a position and a calculatedattribute (or attribute value).

The attribute transformer 40007 may search for neighboring pointsexisting within a specific position/radius from the position of thecenter of each voxel based on the K-D tree or the Morton code. The K-Dtree is a binary search tree and supports a data structure capable ofmanaging points based on the positions such that nearest neighbor search(NNS) can be performed quickly. The Morton code is generated bypresenting coordinates (e.g., (x, y, z)) representing 3D positions ofall points as bit values and mixing the bits. For example, when thecoordinates representing the position of a point are (5, 9, 1), the bitvalues for the coordinates are (0101, 1001, 0001). Mixing the bit valuesaccording to the bit index in order of z, y, and x yields 010001000111.This value is expressed as a decimal number of 1095. That is, the Mortoncode value of the point having coordinates (5, 9, 1) is 1095. Theattribute transformer 40007 may order the points based on the Mortoncode values and perform NNS through a depth-first traversal process.After the attribute transformation operation, the K-D tree or the Mortoncode is used when the NNS is needed in another transformation processfor attribute coding.

As shown in the figure, the transformed attributes are input to the RAHTtransformer 40008 and/or the LOD generator 40009.

The RAHT transformer 40008 according to the embodiments performs RAHTcoding for predicting attribute information based on the reconstructedgeometry information. For example, the RAHT transformer 40008 maypredict attribute information of a node at a higher level in the octreebased on the attribute information associated with a node at a lowerlevel in the octree.

The LOD generator 40009 according to the embodiments generates a levelof detail (LOD). The LOD according to the embodiments is a degree ofdetail of point cloud content. As the LOD value decrease, it indicatesthat the detail of the point cloud content is degraded. As the LOD valueincreases, it indicates that the detail of the point cloud content isenhanced. Points may be classified by the LOD.

The lifting transformer 40010 according to the embodiments performslifting transform coding of transforming the attributes a point cloudbased on weights. As described above, lifting transform coding may beoptionally applied.

The coefficient quantizer 40011 according to the embodiments quantizesthe attribute-coded attributes based on coefficients.

The arithmetic encoder 40012 according to the embodiments encodes thequantized attributes based on arithmetic coding.

Although not shown in the figure, the elements of the point cloud videoencoder of FIG. 4 may be implemented by hardware including one or moreprocessors or integrated circuits configured to communicate with one ormore memories included in the point cloud content providing apparatus,software, firmware, or a combination thereof. The one or more processorsmay perform at least one of the operations and/or functions of theelements of the point cloud video encoder of FIG. 4 described above.Additionally, the one or more processors may operate or execute a set ofsoftware programs and/or instructions for performing the operationsand/or functions of the elements of the point cloud video encoder ofFIG. 4 . The one or more memories according to the embodiments mayinclude a high speed random access memory, or include a non-volatilememory (e.g., one or more magnetic disk storage devices, flash memorydevices, or other non-volatile solid-state memory devices).

FIG. 5 shows an example of voxels according to embodiments.

FIG. 5 shows voxels positioned in a 3D space represented by a coordinatesystem composed of three axes, which are the X-axis, the Y-axis, and theZ-axis. As described with reference to FIG. 4 , the point cloud videoencoder (e.g., the quantizer 40001) may perform voxelization. Voxelrefers to a 3D cubic space generated when a 3D space is divided intounits (unit=1.0) based on the axes representing the 3D space (e.g.,X-axis, Y-axis, and Z-axis). FIG. 5 shows an example of voxels generatedthrough an octree structure in which a cubical axis-aligned bounding boxdefined by two poles (0, 0, 0) and (2^(d), 2^(d), 2^(d)) is recursivelysubdivided. One voxel includes at least one point. The spatialcoordinates of a voxel may be estimated from the positional relationshipwith a voxel group. As described above, a voxel has an attribute (suchas color or reflectance) like pixels of a 2D image/video. The details ofthe voxel are the same as those described with reference to FIG. 4 , andtherefore a description thereof is omitted.

FIG. 6 shows an example of an octree and occupancy code according toembodiments.

As described with reference to FIGS. 1 to 4 , the point cloud contentproviding system (point cloud video encoder 10002) or the octreeanalyzer 40002 of the point cloud video encoder performs octree geometrycoding (or octree coding) based on an octree structure to efficientlymanage the region and/or position of the voxel.

The upper part of FIG. 6 shows an octree structure. The 3D space of thepoint cloud content according to the embodiments is represented by axes(e.g., X-axis, Y-axis, and Z-axis) of the coordinate system. The octreestructure is created by recursive subdividing of a cubical axis-alignedbounding box defined by two poles (0, 0, 0) and (2^(d), 2^(d), 2^(d)).Here, 2^(d) may be set to a value constituting the smallest bounding boxsurrounding all points of the point cloud content (or point cloudvideo). Here, d denotes the depth of the octree. The value of d isdetermined in Equation 1. In Equation 1, (x^(int) _(n), y^(int) _(n),z^(int) _(n)) denotes the positions (or position values) of quantizedpoints.d=Ceil(Log 2(Max(x ^(int) _(n) ,y ^(int) _(n) ,z ^(int) _(n) ,n=1, . . .,N)+1))  Equation 1

As shown in the middle of the upper part of FIG. 6 , the entire 3D spacemay be divided into eight spaces according to partition. Each dividedspace is represented by a cube with six faces. As shown in the upperright of FIG. 6 , each of the eight spaces is divided again based on theaxes of the coordinate system (e.g., X-axis, Y-axis, and Z-axis).Accordingly, each space is divided into eight smaller spaces. Thedivided smaller space is also represented by a cube with six faces. Thispartitioning scheme is applied until the leaf node of the octree becomesa voxel.

The lower part of FIG. 6 shows an octree occupancy code. The occupancycode of the octree is generated to indicate whether each of the eightdivided spaces generated by dividing one space contains at least onepoint. Accordingly, a single occupancy code is represented by eightchild nodes. Each child node represents the occupancy of a dividedspace, and the child node has a value in 1 bit. Accordingly, theoccupancy code is represented as an 8-bit code. That is, when at leastone point is contained in the space corresponding to a child node, thenode is assigned a value of 1. When no point is contained in the spacecorresponding to the child node (the space is empty), the node isassigned a value of 0. Since the occupancy code shown in FIG. 6 is00100001, it indicates that the spaces corresponding to the third childnode and the eighth child node among the eight child nodes each containat least one point. As shown in the figure, each of the third child nodeand the eighth child node has eight child nodes, and the child nodes arerepresented by an 8-bit occupancy code. The figure shows that theoccupancy code of the third child node is 10000111, and the occupancycode of the eighth child node is 01001111. The point cloud video encoder(for example, the arithmetic encoder 40004) according to the embodimentsmay perform entropy encoding on the occupancy codes. In order toincrease the compression efficiency, the point cloud video encoder mayperform intra/inter-coding on the occupancy codes. The reception device(for example, the reception device 10004 or the point cloud videodecoder 10006) according to the embodiments reconstructs the octreebased on the occupancy codes.

The point cloud video encoder (for example, the octree analyzer 40002)according to the embodiments may perform voxelization and octree codingto store the positions of points. However, points are not always evenlydistributed in the 3D space, and accordingly there may be a specificregion in which fewer points are present. Accordingly, it is inefficientto perform voxelization for the entire 3D space. For example, when aspecific region contains few points, voxelization does not need to beperformed in the specific region.

Accordingly, for the above-described specific region (or a node otherthan the leaf node of the octree), the point cloud video encoderaccording to the embodiments may skip voxelization and perform directcoding to directly code the positions of points included in the specificregion. The coordinates of a direct coding point according to theembodiments are referred to as direct coding mode (DCM). The point cloudvideo encoder according to the embodiments may also perform trisoupgeometry encoding, which is to reconstruct the positions of the pointsin the specific region (or node) based on voxels, based on a surfacemodel. The trisoup geometry encoding is geometry encoding thatrepresents an object as a series of triangular meshes. Accordingly, thepoint cloud video decoder may generate a point cloud from the meshsurface. The direct coding and trisoup geometry encoding according tothe embodiments may be selectively performed. In addition, the directcoding and trisoup geometry encoding according to the embodiments may beperformed in combination with octree geometry coding (or octree coding).

To perform direct coding, the option to use the direct mode for applyingdirect coding should be activated. A node to which direct coding is tobe applied is not a leaf node, and points less than a threshold shouldbe present within a specific node. In addition, the total number ofpoints to which direct coding is to be applied should not exceed apreset threshold. When the conditions above are satisfied, the pointcloud video encoder (or the arithmetic encoder 40004) according to theembodiments may perform entropy coding on the positions (or positionvalues) of the points.

The point cloud video encoder (for example, the surface approximationanalyzer 40003) according to the embodiments may determine a specificlevel of the octree (a level less than the depth d of the octree), andthe surface model may be used staring with that level to perform trisoupgeometry encoding to reconstruct the positions of points in the regionof the node based on voxels (Trisoup mode). The point cloud videoencoder according to the embodiments may specify a level at whichtrisoup geometry encoding is to be applied. For example, when thespecific level is equal to the depth of the octree, the point cloudvideo encoder does not operate in the trisoup mode. In other words, thepoint cloud video encoder according to the embodiments may operate inthe trisoup mode only when the specified level is less than the value ofdepth of the octree. The 3D cube region of the nodes at the specifiedlevel according to the embodiments is called a block. One block mayinclude one or more voxels. The block or voxel may correspond to abrick. Geometry is represented as a surface within each block. Thesurface according to embodiments may intersect with each edge of a blockat most once.

One block has 12 edges, and accordingly there are at least 12intersections in one block. Each intersection is called a vertex (orapex). A vertex present along an edge is detected when there is at leastone occupied voxel adjacent to the edge among all blocks sharing theedge. The occupied voxel according to the embodiments refers to a voxelcontaining a point. The position of the vertex detected along the edgeis the average position along the edge of all voxels adjacent to theedge among all blocks sharing the edge.

Once the vertex is detected, the point cloud video encoder according tothe embodiments may perform entropy encoding on the starting point (x,y, z) of the edge, the direction vector (Δx, Δy, Δz) of the edge, andthe vertex position value (relative position value within the edge).When the trisoup geometry encoding is applied, the point cloud videoencoder according to the embodiments (for example, the geometryreconstructor 40005) may generate restored geometry (reconstructedgeometry) by performing the triangle reconstruction, up-sampling, andvoxelization processes.

The vertices positioned at the edge of the block determine a surfacethat passes through the block. The surface according to the embodimentsis a non-planar polygon. In the triangle reconstruction process, asurface represented by a triangle is reconstructed based on the startingpoint of the edge, the direction vector of the edge, and the positionvalues of the vertices. The triangle reconstruction process is performedaccording to Equation 2 by: i) calculating the centroid value of eachvertex, ii) subtracting the center value from each vertex value, andiii) estimating the sum of the squares of the values obtained by thesubtraction.

$\begin{matrix}{\begin{bmatrix}\mu_{x} \\\mu_{y} \\\mu_{z}\end{bmatrix} = {{\frac{1}{n}{{{\sum}_{i - 1}^{n}\begin{bmatrix}x_{i} \\y_{i} \\z_{i}\end{bmatrix}}\begin{bmatrix}{\overset{\_}{x}}_{i} \\{\overset{\_}{y}}_{i} \\{\overset{\_}{z}}_{i}\end{bmatrix}}} = {{\begin{bmatrix}x_{i} \\y_{i} \\z_{i}\end{bmatrix} - {\begin{bmatrix}\mu_{x} \\\mu_{y} \\\mu_{z}\end{bmatrix}\begin{bmatrix}\sigma_{x}^{2} \\\sigma_{y}^{2} \\\sigma_{z}^{2}\end{bmatrix}}} = {{\sum}_{i - 1}^{n}\begin{bmatrix}{\overset{\_}{x}}_{i}^{2} \\{\overset{\_}{y}}_{i}^{2} \\{\overset{\_}{z}}_{i}^{2}\end{bmatrix}}}}} & {{Equation}2}\end{matrix}$

Then, the minimum value of the sum is estimated, and the projectionprocess is performed according to the axis with the minimum value. Forexample, when the element x is the minimum, each vertex is projected onthe x-axis with respect to the center of the block, and projected on the(y, z) plane. When the values obtained through projection on the (y, z)plane are (ai, bi), the value of θ is estimated through a tan 2(bi, ai),and the vertices are ordered based on the value of θ. The table 1 belowshows a combination of vertices for creating a triangle according to thenumber of the vertices. The vertices are ordered from 1 to n. The table1 below shows that for four vertices, two triangles may be constructedaccording to combinations of vertices. The first triangle may consist ofvertices 1, 2, and 3 among the ordered vertices, and the second trianglemay consist of vertices 3, 4, and 1 among the ordered vertices.

TABLE 1 Triangles formed from vertices ordered 1, . . . , n n Triangles3 (1, 2, 3) 4 (1, 2, 3), (3, 4, 1) 5 (1, 2, 3), (3, 4, 5), (5, 1, 3) 6(1, 2, 3), (3, 4, 5), (5, 6, 1), (1, 3, 5) 7 (1, 2, 3), (3, 4, 5), (5,6, 7), (7, 1, 3), (3, 5, 7) 8 (1, 2, 3), (3, 4, 5), (5, 6, 7), (7, 8,1), (1, 3, 5), (5, 7, 1) 9 (1, 2, 3), (3, 4, 5), (5, 6, 7), (7, 8, 9),(9, 1, 3), (3, 5, 7), (7, 9, 3) 10 (1, 2, 3), (3, 4, 5), (5, 6, 7), (7,8, 9), (9, 10, 1), (1, 3, 5), (5, 7, 9), (9, 1, 5) 11 (1, 2, 3), (3, 4,5), (5, 6, 7), (7, 8, 9), (9, 10, 11), (11, 1, 3), (3, 5, 7), (7, 9,11), (11, 3, 7) 12 (1, 2, 3), (3, 4, 5), (5, 6, 7), (7, 8, 9), (9, 10,11), (11, 12, 1), (1, 3, 5), (5, 7, 9), (9, 11, 1), (1, 5, 9)

The upsampling process is performed to add points in the middle alongthe edge of the triangle and perform voxelization. The added points aregenerated based on the upsampling factor and the width of the block. Theadded points are called refined vertices. The point cloud video encoderaccording to the embodiments may voxelize the refined vertices. Inaddition, the point cloud video encoder may perform attribute encodingbased on the voxelized positions (or position values).

FIG. 7 shows an example of a neighbor node pattern according toembodiments.

In order to increase the compression efficiency of the point cloudvideo, the point cloud video encoder according to the embodiments mayperform entropy coding based on context adaptive arithmetic coding.

As described with reference to FIGS. 1 to 6 , the point cloud contentproviding system or the point cloud video encoder 10002 of FIG. 1 , orthe point cloud video encoder or arithmetic encoder 40004 of FIG. 4 mayperform entropy coding on the occupancy code immediately. In addition,the point cloud content providing system or the point cloud videoencoder may perform entropy encoding (intra encoding) based on theoccupancy code of the current node and the occupancy of neighboringnodes, or perform entropy encoding (inter encoding) based on theoccupancy code of the previous frame. A frame according to embodimentsrepresents a set of point cloud videos generated at the same time. Thecompression efficiency of intra encoding/inter encoding according to theembodiments may depend on the number of neighboring nodes that arereferenced. When the bits increase, the operation becomes complicated,but the encoding may be biased to one side, which may increase thecompression efficiency. For example, when a 3-bit context is given,coding needs to be performed using 2³=8 methods. The part divided forcoding affects the complexity of implementation. Accordingly, it isnecessary to meet an appropriate level of compression efficiency andcomplexity.

FIG. 7 illustrates a process of obtaining an occupancy pattern based onthe occupancy of neighbor nodes. The point cloud video encoder accordingto the embodiments determines occupancy of neighbor nodes of each nodeof the octree and obtains a value of a neighbor pattern. The neighbornode pattern is used to infer the occupancy pattern of the node. The uppart of FIG. 7 shows a cube corresponding to a node (a cube positionedin the middle) and six cubes (neighbor nodes) sharing at least one facewith the cube. The nodes shown in the figure are nodes of the samedepth. The numbers shown in the figure represent weights (1, 2, 4, 8,16, and 32) associated with the six nodes, respectively. The weights areassigned sequentially according to the positions of neighboring nodes.

The down part of FIG. 7 shows neighbor node pattern values. A neighbornode pattern value is the sum of values multiplied by the weight of anoccupied neighbor node (a neighbor node having a point). Accordingly,the neighbor node pattern values are 0 to 63. When the neighbor nodepattern value is 0, it indicates that there is no node having a point(no occupied node) among the neighbor nodes of the node. When theneighbor node pattern value is 63, it indicates that all neighbor nodesare occupied nodes. As shown in the figure, since neighbor nodes towhich weights 1, 2, 4, and 8 are assigned are occupied nodes, theneighbor node pattern value is 15, the sum of 1, 2, 4, and 8. The pointcloud video encoder may perform coding according to the neighbor nodepattern value (for example, when the neighbor node pattern value is 63,64 kinds of coding may be performed). According to embodiments, thepoint cloud video encoder may reduce coding complexity by changing aneighbor node pattern value (for example, based on a table by which 64is changed to 10 or 6).

FIG. 8 illustrates an example of point configuration in each LODaccording to embodiments.

As described with reference to FIGS. 1 to 7 , encoded geometry isreconstructed (decompressed) before attribute encoding is performed.When direct coding is applied, the geometry reconstruction operation mayinclude changing the placement of direct coded points (e.g., placing thedirect coded points in front of the point cloud data). When trisoupgeometry encoding is applied, the geometry reconstruction process isperformed through triangle reconstruction, up-sampling, andvoxelization. Since the attribute depends on the geometry, attributeencoding is performed based on the reconstructed geometry.

The point cloud video encoder (for example, the LOD generator 40009) mayclassify (reorganize or group) points by LOD. FIG. 8 shows the pointcloud content corresponding to LODs. The leftmost picture in FIG. 8represents original point cloud content. The second picture from theleft of FIG. 8 represents distribution of the points in the lowest LOD,and the rightmost picture in FIG. 8 represents distribution of thepoints in the highest LOD. That is, the points in the lowest LOD aresparsely distributed, and the points in the highest LOD are denselydistributed. That is, as the LOD rises in the direction pointed by thearrow indicated at the bottom of FIG. 8 , the space (or distance)between points is narrowed.

FIG. 9 illustrates an example of point configuration for each LODaccording to embodiments.

As described with reference to FIGS. 1 to 8 , the point cloud contentproviding system, or the point cloud video encoder (for example, thepoint cloud video encoder 10002 of FIG. 1 , the point cloud videoencoder of FIG. 4 , or the LOD generator 40009) may generates an LOD.The LOD is generated by reorganizing the points into a set of refinementlevels according to a set LOD distance value (or a set of Euclideandistances). The LOD generation process is performed not only by thepoint cloud video encoder, but also by the point cloud video decoder.

The upper part of FIG. 9 shows examples (P0 to P9) of points of thepoint cloud content distributed in a 3D space. In FIG. 9 , the originalorder represents the order of points P0 to P9 before LOD generation. InFIG. 9 , the LOD based order represents the order of points according tothe LOD generation. Points are reorganized by LOD. Also, a high LODcontains the points belonging to lower LODs. As shown in FIG. 9 , LOD0contains P0, P5, P4 and P2. LOD1 contains the points of LOD0, P1, P6 andP3. LOD2 contains the points of LOD0, the points of LOD1, P9, P8 and P7.

As described with reference to FIG. 4 , the point cloud video encoderaccording to the embodiments may perform prediction transform codingbased on LOD, lifting transform coding based on LOD, and RAHT transformcoding selectively or in combination.

The point cloud video encoder according to the embodiments may generatea predictor for points to perform prediction transform coding based onLOD for setting a predicted attribute (or predicted attribute value) ofeach point. That is, N predictors may be generated for N points. Thepredictor according to the embodiments may calculate a weight(=1/distance) based on the LOD value of each point, indexing informationabout neighboring points present within a set distance for each LOD, anda distance to the neighboring points.

The predicted attribute (or attribute value) according to theembodiments is set to the average of values obtained by multiplying theattributes (or attribute values) (e.g., color, reflectance, etc.) ofneighbor points set in the predictor of each point by a weight (orweight value) calculated based on the distance to each neighbor point.The point cloud video encoder according to the embodiments (for example,the coefficient quantizer 40011) may quantize and inversely quantize theresidual of each point (which may be called residual attribute, residualattribute value, attribute prediction residual value or prediction errorattribute value and so on) obtained by subtracting a predicted attribute(or attribute value) each point from the attribute (i.e., originalattribute value) of each point. The quantization process performed for aresidual attribute value in a transmission device is configured as shownin table 2. The inverse quantization process performed for a residualattribute value in a reception device is configured as shown in table 3.

TABLE 2   int PCCQuantization(int value, int quantStep) { if( value >=0){ return floor(value / quantStep + 1.0 / 3.0); } else { return−floor(−value / quantStep + 1.0 / 3.0); } }

TABLE 3   int PCCInverseQuantization(int value, int quantStep) { if(quantStep ==0) { return value; } else { return value * quantStep; } }

When the predictor of each point has neighbor points, the point cloudvideo encoder (e.g., the arithmetic encoder 40012) according to theembodiments may perform entropy coding on the quantized and inverselyquantized residual attribute values as described above. When thepredictor of each point has no neighbor point, the point cloud videoencoder according to the embodiments (for example, the arithmeticencoder 40012) may perform entropy coding on the attributes of thecorresponding point without performing the above-described operation.

The point cloud video encoder according to the embodiments (for example,the lifting transformer 40010) may generate a predictor of each point,set the calculated LOD and register neighbor points in the predictor,and set weights according to the distances to neighbor points to performlifting transform coding. The lifting transform coding according to theembodiments is similar to the above-described prediction transformcoding, but differs therefrom in that weights are cumulatively appliedto attribute values. The process of cumulatively applying weights to theattribute values according to embodiments is configured as follows.

1) Create an array Quantization Weight (QW) for storing the weight valueof each point. The initial value of all elements of QW is 1.0. Multiplythe QW values of the predictor indexes of the neighbor nodes registeredin the predictor by the weight of the predictor of the current point,and add the values obtained by the multiplication.

2) Lift prediction process: Subtract the value obtained by multiplyingthe attribute value of the point by the weight from the existingattribute value to calculate a predicted attribute value.

3) Create temporary arrays called updateweight and update and initializethe temporary arrays to zero.

4) Cumulatively add the weights calculated by multiplying the weightscalculated for all predictors by a weight stored in the QW correspondingto a predictor index to the updateweight array as indexes of neighbornodes. Cumulatively add, to the update array, a value obtained bymultiplying the attribute value of the index of a neighbor node by thecalculated weight.

5) Lift update process: Divide the attribute values of the update arrayfor all predictors by the weight value of the updateweight array of thepredictor index, and add the existing attribute value to the valuesobtained by the division.

6) Calculate predicted attributes by multiplying the attribute valuesupdated through the lift update process by the weight updated throughthe lift prediction process (stored in the QW) for all predictors. Thepoint cloud video encoder (e.g., coefficient quantizer 40011) accordingto the embodiments quantizes the predicted attribute values. Inaddition, the point cloud video encoder (e.g., the arithmetic encoder40012) performs entropy coding on the quantized attribute values.

The point cloud video encoder (for example, the RAHT transformer 40008)according to the embodiments may perform RAHT transform coding in whichattributes of nodes of a higher level are predicted using the attributesassociated with nodes of a lower level in the octree. RAHT transformcoding is an example of attribute intra coding through an octreebackward scan. The point cloud video encoder according to theembodiments scans the entire region from the voxel and repeats themerging process of merging the voxels into a larger block at each stepuntil the root node is reached. The merging process according to theembodiments is performed only on the occupied nodes. The merging processis not performed on the empty node. The merging process is performed onan upper node immediately above the empty node.

Equation 3 below represents a RAHT transformation matrix. In Equation 3,g_(l) _(x,y,z) denotes the average attribute value of voxels at level l.g_(l) _(x,y,z) may be calculated based on g_(l+1) _(2x,y,z) and g_(l+1)_(2x+1,y,z) . The weights for g_(l) _(2x,y,z) and g_(l) _(2x+1,y,z) arew1=w_(l) _(2x,y,z) and w2=w_(l) _(2x+1,y,z) .

$\begin{matrix}{\left\lceil \begin{matrix}g_{l - 1_{x,y,z}} \\h_{l - 1_{x,y,z}}\end{matrix} \right\rceil = {{T_{w1w2}\left\lceil \begin{matrix}g_{l_{{2x},y,z}} \\g_{l_{{{2x} + 1},y,z}}\end{matrix} \right\rceil T_{w1w2}} = {\frac{1}{\sqrt{{w1} + {w2}}}\begin{bmatrix}\sqrt{w1} & \sqrt{w2} \\{- \sqrt{w2}} & \sqrt{w1}\end{bmatrix}}}} & {{Equation}3}\end{matrix}$

Here, g_(l−1) _(x,y,z) is a low-pass value and is used in the mergingprocess at the next higher level. h_(l−1) _(x,y,z) denotes high-passcoefficients. The high-pass coefficients at each step are quantized andsubjected to entropy coding (for example, encoding by the arithmeticencoder 40012). The weights are calculated as w_(l) _(−1x,y,z) =w_(l)_(2x,y,z) +w_(l) _(2x+1,y,z) . The root node is created through the g₁_(0,0,0) and g₁ _(0,0,1) as Equation 4.

$\begin{matrix}{\left\lceil \begin{matrix}{gDC} \\h_{0_{0,0,0}}\end{matrix} \right\rceil = {T_{w1000w1001}\left\lceil \begin{matrix}g_{1_{0,0,{0z}}} \\g_{1_{0,0,1}}\end{matrix} \right\rceil}} & {{Equation}4}\end{matrix}$

The value of gDC is also quantized and subjected to entropy coding likethe high-pass coefficients.

FIG. 10 illustrates a point cloud video decoder according toembodiments.

The point cloud video decoder illustrated in FIG. 10 is an example ofthe point cloud video decoder 10006 described in FIG. 1 , and mayperform the same or similar operations as the operations of the pointcloud video decoder 10006 illustrated in FIG. 1 . As shown in thefigure, the point cloud video decoder may receive a geometry bitstreamand an attribute bitstream contained in one or more bitstreams. Thepoint cloud video decoder includes a geometry decoder and an attributedecoder. The geometry decoder performs geometry decoding on the geometrybitstream and outputs decoded geometry. The attribute decoder performsattribute decoding on the attribute bitstream based on the decodedgeometry, and outputs decoded attributes. The decoded geometry anddecoded attributes are used to reconstruct point cloud content (adecoded point cloud).

FIG. 11 illustrates a point cloud video decoder according toembodiments.

The point cloud video decoder illustrated in FIG. 11 is an example ofthe point cloud video decoder illustrated in FIG. 10 , and may perform adecoding operation, which is an inverse process of the encodingoperation of the point cloud video encoder illustrated in FIGS. 1 to 9 .

As described with reference to FIGS. 1 and 10 , the point cloud videodecoder may perform geometry decoding and attribute decoding. Thegeometry decoding is performed before the attribute decoding.

The point cloud video decoder according to the embodiments includes anarithmetic decoder (Arithmetic decode) 11000, an octree synthesizer(Synthesize octree) 11001, a surface approximation synthesizer(Synthesize surface approximation) 11002, and a geometry reconstructor(Reconstruct geometry) 11003, a coordinate inverse transformer (Inversetransform coordinates) 11004, an arithmetic decoder (Arithmetic decode)11005, an inverse quantizer (Inverse quantize) 11006, a RAHT transformer11007, an LOD generator (Generate LOD) 11008, an inverse lifter (inverselifting) 11009, and/or a color inverse transformer (Inverse transformcolors) 11010.

The arithmetic decoder 11000, the octree synthesizer 11001, the surfaceapproximation synthesizer 11002, and the geometry reconstructor 11003,and the coordinate inverse transformer 11004 may perform geometrydecoding. The geometry decoding according to the embodiments may includedirect decoding and trisoup geometry decoding. The direct decoding andtrisoup geometry decoding are selectively applied. The geometry decodingis not limited to the above-described example, and is performed as aninverse process of the geometry encoding described with reference toFIGS. 1 to 9 .

The arithmetic decoder 11000 according to the embodiments decodes thereceived geometry bitstream based on the arithmetic coding. Theoperation of the arithmetic decoder 11000 corresponds to the inverseprocess of the arithmetic encoder 40004.

The octree synthesizer 11001 according to the embodiments may generatean octree by acquiring an occupancy code from the decoded geometrybitstream (or information on the geometry secured as a result ofdecoding). The occupancy code is configured as described in detail withreference to FIGS. 1 to 9 .

When the trisoup geometry encoding is applied, the surface approximationsynthesizer 11002 according to the embodiments may synthesize a surfacebased on the decoded geometry and/or the generated octree.

The geometry reconstructor 11003 according to the embodiments mayregenerate geometry based on the surface and/or the decoded geometry. Asdescribed with reference to FIGS. 1 to 9 , direct coding and trisoupgeometry encoding are selectively applied. Accordingly, the geometryreconstructor 11003 directly imports and adds position information aboutthe points to which direct coding is applied. When the trisoup geometryencoding is applied, the geometry reconstructor 11003 may reconstructthe geometry by performing the reconstruction operations of the geometryreconstructor 40005, for example, triangle reconstruction, up-sampling,and voxelization. Details are the same as those described with referenceto FIG. 6 , and thus description thereof is omitted. The reconstructedgeometry may include a point cloud picture or frame that does notcontain attributes.

The coordinate inverse transformer 11004 according to the embodimentsmay acquire positions of the points by transforming the coordinatesbased on the reconstructed geometry.

The arithmetic decoder 11005, the inverse quantizer 11006, the RAHTtransformer 11007, the LOD generator 11008, the inverse lifter 11009,and/or the color inverse transformer 11010 may perform the attributedecoding described with reference to FIG. 10 . The attribute decodingaccording to the embodiments includes region adaptive hierarchicaltransform (RAHT) decoding, interpolation-based hierarchicalnearest-neighbor prediction (prediction transform) decoding, andinterpolation-based hierarchical nearest-neighbor prediction with anupdate/lifting step (lifting transform) decoding. The three decodingschemes described above may be used selectively, or a combination of oneor more decoding schemes may be used. The attribute decoding accordingto the embodiments is not limited to the above-described example.

The arithmetic decoder 11005 according to the embodiments decodes theattribute bitstream by arithmetic coding.

The inverse quantizer 11006 according to the embodiments inverselyquantizes the information about the decoded attribute bitstream orattributes secured as a result of the decoding, and outputs theinversely quantized attributes (or attribute values). The inversequantization may be selectively applied based on the attribute encodingof the point cloud video encoder.

According to embodiments, the RAHT transformer 11007, the LOD generator11008, and/or the inverse lifter 11009 may process the reconstructedgeometry and the inversely quantized attributes. As described above, theRAHT transformer 11007, the LOD generator 11008, and/or the inverselifter 11009 may selectively perform a decoding operation correspondingto the encoding of the point cloud video encoder.

The color inverse transformer 11010 according to the embodimentsperforms inverse transform coding to inversely transform a color value(or texture) included in the decoded attributes. The operation of thecolor inverse transformer 11010 may be selectively performed based onthe operation of the color transformer 40006 of the point cloud videoencoder.

Although not shown in the figure, the elements of the point cloud videodecoder of FIG. 11 may be implemented by hardware including one or moreprocessors or integrated circuits configured to communicate with one ormore memories included in the point cloud content providing apparatus,software, firmware, or a combination thereof. The one or more processorsmay perform at least one or more of the operations and/or functions ofthe elements of the point cloud video decoder of FIG. 11 describedabove. Additionally, the one or more processors may operate or execute aset of software programs and/or instructions for performing theoperations and/or functions of the elements of the point cloud videodecoder of FIG. 11 .

FIG. 12 illustrates a transmission device according to embodiments.

The transmission device shown in FIG. 12 is an example of thetransmission device 10000 of FIG. 1 (or the point cloud video encoder ofFIG. 4 ). The transmission device illustrated in FIG. 12 may perform oneor more of the operations and methods the same as or similar to those ofthe point cloud video encoder described with reference to FIGS. 1 to 9 .The transmission device according to the embodiments may include a datainput unit 12000, a quantization processor 12001, a voxelizationprocessor 12002, an octree occupancy code generator 12003, a surfacemodel processor 12004, an intra/inter-coding processor 12005, anarithmetic coder 12006, a metadata processor 12007, a color transformprocessor 12008, an attribute transform processor 12009, aprediction/lifting/RAHT transform processor 12010, an arithmetic coder12011 and/or a transmission processor 12012.

The data input unit 12000 according to the embodiments receives oracquires point cloud data. The data input unit 12000 may perform anoperation and/or acquisition method the same as or similar to theoperation and/or acquisition method of the point cloud video acquisitionunit 10001 (or the acquisition process 20000 described with reference toFIG. 2 ).

The data input unit 12000, the quantization processor 12001, thevoxelization processor 12002, the octree occupancy code generator 12003,the surface model processor 12004, the intra/inter-coding processor12005, and the arithmetic coder 12006 perform geometry encoding. Thegeometry encoding according to the embodiments is the same as or similarto the geometry encoding described with reference to FIGS. 1 to 9 , andthus a detailed description thereof is omitted.

The quantization processor 12001 according to the embodiments quantizesgeometry (e.g., position values of points). The operation and/orquantization of the quantization processor 12001 is the same as orsimilar to the operation and/or quantization of the quantizer 40001described with reference to FIG. 4 . Details are the same as thosedescribed with reference to FIGS. 1 to 9 .

The voxelization processor 12002 according to embodiments voxelizes thequantized position values of the points. The voxelization processor12002 may perform an operation and/or process the same or similar to theoperation and/or the voxelization process of the quantizer 40001described with reference to FIG. 4 . Details are the same as thosedescribed with reference to FIGS. 1 to 9 .

The octree occupancy code generator 12003 according to the embodimentsperforms octree coding on the voxelized positions of the points based onan octree structure. The octree occupancy code generator 12003 maygenerate an occupancy code. The octree occupancy code generator 12003may perform an operation and/or method the same as or similar to theoperation and/or method of the point cloud video encoder (or the octreeanalyzer 40002) described with reference to FIGS. 4 and 6 . Details arethe same as those described with reference to FIGS. 1 to 9 .

The surface model processor 12004 according to the embodiments mayperform trigsoup geometry encoding based on a surface model toreconstruct the positions of points in a specific region (or node) on avoxel basis. The surface model processor 12004 may perform an operationand/or method the same as or similar to the operation and/or method ofthe point cloud video encoder (for example, the surface approximationanalyzer 40003) described with reference to FIG. 4 . Details are thesame as those described with reference to FIGS. 1 to 9 .

The intra/inter-coding processor 12005 according to the embodiments mayperform intra/inter-coding on point cloud data. The intra/inter-codingprocessor 12005 may perform coding the same as or similar to theintra/inter-coding described with reference to FIG. 7 . Details are thesame as those described with reference to FIG. 7 . According toembodiments, the intra/inter-coding processor 12005 may be included inthe arithmetic coder 12006.

The arithmetic coder 12006 according to the embodiments performs entropyencoding on an octree of the point cloud data and/or an approximatedoctree. For example, the encoding scheme includes arithmetic encoding.The arithmetic coder 12006 performs an operation and/or method the sameas or similar to the operation and/or method of the arithmetic encoder40004.

The metadata processor 12007 according to the embodiments processesmetadata about the point cloud data, for example, a set value, andprovides the same to a necessary processing process such as geometryencoding and/or attribute encoding. Also, the metadata processor 12007according to the embodiments may generate and/or process signalinginformation related to the geometry encoding and/or the attributeencoding. The signaling information according to the embodiments may beencoded separately from the geometry encoding and/or the attributeencoding. The signaling information according to the embodiments may beinterleaved.

The color transform processor 12008, the attribute transform processor12009, the prediction/lifting/RAHT transform processor 12010, and thearithmetic coder 12011 perform the attribute encoding. The attributeencoding according to the embodiments is the same as or similar to theattribute encoding described with reference to FIGS. 1 to 9 , and thus adetailed description thereof is omitted.

The color transform processor 12008 according to the embodimentsperforms color transform coding to transform color values included inattributes. The color transform processor 12008 may perform colortransform coding based on the reconstructed geometry. The reconstructedgeometry is the same as described with reference to FIGS. 1 to 9 . Also,it performs an operation and/or method the same as or similar to theoperation and/or method of the color transformer 40006 described withreference to FIG. 4 is performed. The detailed description thereof isomitted.

The attribute transform processor 12009 according to the embodimentsperforms attribute transformation to transform the attributes based onthe reconstructed geometry and/or the positions on which geometryencoding is not performed. The attribute transform processor 12009performs an operation and/or method the same as or similar to theoperation and/or method of the attribute transformer 40007 describedwith reference to FIG. 4 . The detailed description thereof is omitted.The prediction/lifting/RAHT transform processor 12010 according to theembodiments may code the transformed attributes by any one or acombination of RAHT coding, prediction transform coding, and liftingtransform coding. The prediction/lifting/RAHT transform processor 12010performs at least one of the operations the same as or similar to theoperations of the RAHT transformer 40008, the LOD generator 40009, andthe lifting transformer 40010 described with reference to FIG. 4 . Inaddition, the prediction transform coding, the lifting transform coding,and the RAHT transform coding are the same as those described withreference to FIGS. 1 to 9 , and thus a detailed description thereof isomitted.

The arithmetic coder 12011 according to the embodiments may encode thecoded attributes based on the arithmetic coding. The arithmetic coder12011 performs an operation and/or method the same as or similar to theoperation and/or method of the arithmetic encoder 40012.

The transmission processor 12012 according to the embodiments maytransmit each bitstream containing encoded geometry and/or encodedattributes and metadata, or transmit one bitstream configured with theencoded geometry and/or the encoded attributes and the metadata. Whenthe encoded geometry and/or the encoded attributes and the metadataaccording to the embodiments are configured into one bitstream, thebitstream may include one or more sub-bitstreams. The bitstreamaccording to the embodiments may contain signaling information includinga sequence parameter set (SPS) for signaling of a sequence level, ageometry parameter set (GPS) for signaling of geometry informationcoding, an attribute parameter set (APS) for signaling of attributeinformation coding, and a tile parameter set (TPS or tile inventory) forsignaling of a tile level, and slice data. The slice data may includeinformation about one or more slices. One slice according to embodimentsmay include one geometry bitstream Geom0⁰ and one or more attributebitstreams Attr0⁰ and Attr1⁰.

The slice is a series of a syntax element representing in whole or inpart of the coded point cloud frame.

The TPS according to the embodiments may include information about eachtile (for example, coordinate information and height/size informationabout a bounding box) for one or more tiles. The geometry bitstream maycontain a header and a payload. The header of the geometry bitstreamaccording to the embodiments may contain a parameter set identifier(geom_parameter_set_id), a tile identifier (geom_tile_id) and a sliceidentifier (geom_slice_id) included in the GPS, and information aboutthe data contained in the payload. As described above, the metadataprocessor 12007 according to the embodiments may generate and/or processthe signaling information and transmit the same to the transmissionprocessor 12012. According to embodiments, the elements to performgeometry encoding and the elements to perform attribute encoding mayshare data/information with each other as indicated by dotted lines. Thetransmission processor 12012 according to the embodiments may perform anoperation and/or transmission method the same as or similar to theoperation and/or transmission method of the transmitter 10003. Detailsare the same as those described with reference to FIGS. 1 and 2 , andthus a description thereof is omitted.

FIG. 13 illustrates a reception device according to embodiments.

The reception device illustrated in FIG. 13 is an example of thereception device 10004 of FIG. 1 (or the point cloud video decoder ofFIGS. 10 and 11 ). The reception device illustrated in FIG. 13 mayperform one or more of the operations and methods the same as or similarto those of the point cloud video decoder described with reference toFIGS. 1 to 11 .

The reception device according to the embodiment includes a receiver13000, a reception processor 13001, an arithmetic decoder 13002, anoccupancy code-based octree reconstruction processor 13003, a surfacemodel processor (triangle reconstruction, up-sampling, voxelization)13004, an inverse quantization processor 13005, a metadata parser 13006,an arithmetic decoder 13007, an inverse quantization processor 13008, aprediction/lifting/RAHT inverse transform processor 13009, a colorinverse transform processor 13010, and/or a renderer 13011. Each elementfor decoding according to the embodiments may perform an inverse processof the operation of a corresponding element for encoding according tothe embodiments.

The receiver 13000 according to the embodiments receives point clouddata. The receiver 13000 may perform an operation and/or receptionmethod the same as or similar to the operation and/or reception methodof the receiver 10005 of FIG. 1 . The detailed description thereof isomitted.

The reception processor 13001 according to the embodiments may acquire ageometry bitstream and/or an attribute bitstream from the received data.The reception processor 13001 may be included in the receiver 13000.

The arithmetic decoder 13002, the occupancy code-based octreereconstruction processor 13003, the surface model processor 13004, andthe inverse quantization processor 1305 may perform geometry decoding.The geometry decoding according to embodiments is the same as or similarto the geometry decoding described with reference to FIGS. 1 to 10 , andthus a detailed description thereof is omitted.

The arithmetic decoder 13002 according to the embodiments may decode thegeometry bitstream based on arithmetic coding. The arithmetic decoder13002 performs an operation and/or coding the same as or similar to theoperation and/or coding of the arithmetic decoder 11000.

The occupancy code-based octree reconstruction processor 13003 accordingto the embodiments may reconstruct an octree by acquiring an occupancycode from the decoded geometry bitstream (or information about thegeometry secured as a result of decoding). The occupancy code-basedoctree reconstruction processor 13003 performs an operation and/ormethod the same as or similar to the operation and/or octree generationmethod of the octree synthesizer 11001. When the trisoup geometryencoding is applied, the surface model processor 1302 according to theembodiments may perform trisoup geometry decoding and related geometryreconstruction (for example, triangle reconstruction, up-sampling,voxelization) based on the surface model method. The surface modelprocessor 1302 performs an operation the same as or similar to that ofthe surface approximation synthesizer 11002 and/or the geometryreconstructor 11003.

The inverse quantization processor 1305 according to the embodiments mayinversely quantize the decoded geometry.

The metadata parser 1306 according to the embodiments may parse metadatacontained in the received point cloud data, for example, a set value.The metadata parser 1306 may pass the metadata to geometry decodingand/or attribute decoding. The metadata is the same as that describedwith reference to FIG. 12 , and thus a detailed description thereof isomitted.

The arithmetic decoder 13007, the inverse quantization processor 13008,the prediction/lifting/RAHT inverse transform processor 13009 and thecolor inverse transform processor 13010 perform attribute decoding. Theattribute decoding is the same as or similar to the attribute decodingdescribed with reference to FIGS. 1 to 10 , and thus a detaileddescription thereof is omitted.

The arithmetic decoder 13007 according to the embodiments may decode theattribute bitstream by arithmetic coding. The arithmetic decoder 13007may decode the attribute bitstream based on the reconstructed geometry.The arithmetic decoder 13007 performs an operation and/or coding thesame as or similar to the operation and/or coding of the arithmeticdecoder 11005.

The inverse quantization processor 13008 according to the embodimentsmay inversely quantize the decoded attribute bitstream. The inversequantization processor 13008 performs an operation and/or method thesame as or similar to the operation and/or inverse quantization methodof the inverse quantizer 11006.

The prediction/lifting/RAHT inverse transformer 13009 according to theembodiments may process the reconstructed geometry and the inverselyquantized attributes. The prediction/lifting/RAHT inverse transformprocessor 1301 performs one or more of operations and/or decoding thesame as or similar to the operations and/or decoding of the RAHTtransformer 11007, the LOD generator 11008, and/or the inverse lifter11009. The color inverse transform processor 13010 according to theembodiments performs inverse transform coding to inversely transformcolor values (or textures) included in the decoded attributes. The colorinverse transform processor 13010 performs an operation and/or inversetransform coding the same as or similar to the operation and/or inversetransform coding of the color inverse transformer 11010. The renderer13011 according to the embodiments may render the point cloud data.

FIG. 14 shows an exemplary structure operatively connectable with amethod/device for transmitting and receiving point cloud data accordingto embodiments.

The structure of FIG. 14 represents a configuration in which at leastone of a server 17600, a robot 17100, a self-driving vehicle 17200, anXR device 17300, a smartphone 17400, a home appliance 17500, and/or ahead-mount display (HMD) 17700 is connected to a cloud network 17100.The robot 17100, the self-driving vehicle 17200, the XR device 17300,the smartphone 17400, or the home appliance 17500 is referred to as adevice. In addition, the XR device 17300 may correspond to a point cloudcompressed data (PCC) device according to embodiments or may beoperatively connected to the PCC device.

The cloud network 17000 may represent a network that constitutes part ofthe cloud computing infrastructure or is present in the cloud computinginfrastructure. Here, the cloud network 17000 may be configured using a3G network, 4G or Long Term Evolution (LTE) network, or a 5G network.

The server 17600 may be connected to at least one of the robot 17100,the self-driving vehicle 17200, the XR device 17300, the smartphone17400, the home appliance 17500, and/or the HMD 17700 over the cloudnetwork 17000 and may assist in at least a part of the processing of theconnected devices 17100 to 17700.

The HMD 17700 represents one of the implementation types of the XRdevice and/or the PCC device according to the embodiments. The HMD typedevice according to the embodiments includes a communication unit, acontrol unit, a memory, an I/O unit, a sensor unit, and a power supplyunit.

Hereinafter, various embodiments of the devices 17100 to 17500 to whichthe above-described technology is applied will be described. The devices17100 to 17500 illustrated in FIG. 14 may be operativelyconnected/coupled to a point cloud data transmission device andreception according to the above-described embodiments.

<PCC+XR>

The XR/PCC device 17300 may employ PCC technology and/or XR (AR+VR)technology, and may be implemented as an HMD, a head-up display (HUD)provided in a vehicle, a television, a mobile phone, a smartphone, acomputer, a wearable device, a home appliance, a digital signage, avehicle, a stationary robot, or a mobile robot.

The XR/PCC device 17300 may analyze 3D point cloud data or image dataacquired through various sensors or from an external device and generateposition data and attribute data about 3D points. Thereby, the XR/PCCdevice 17300 may acquire information about the surrounding space or areal object, and render and output an XR object. For example, the XR/PCCdevice 17300 may match an XR object including auxiliary informationabout a recognized object with the recognized object and output thematched XR object.

<PCC+Self-Driving+XR>

The self-driving vehicle 17200 may be implemented as a mobile robot, avehicle, an unmanned aerial vehicle, or the like by applying the PCCtechnology and the XR technology.

The self-driving vehicle 17200 to which the XR/PCC technology is appliedmay represent a self-driving vehicle provided with means for providingan XR image, or a self-driving vehicle that is a target ofcontrol/interaction in the XR image. In particular, the self-drivingvehicle 17200 which is a target of control/interaction in the XR imagemay be distinguished from the XR device 17300 and may be operativelyconnected thereto.

The self-driving vehicle 17200 having means for providing an XR/PCCimage may acquire sensor information from sensors including a camera,and output the generated XR/PCC image based on the acquired sensorinformation. For example, the self-driving vehicle 17200 may have an HUDand output an XR/PCC image thereto, thereby providing an occupant withan XR/PCC object corresponding to a real object or an object present onthe screen.

When the XR/PCC object is output to the HUD, at least a part of theXR/PCC object may be output to overlap the real object to which theoccupant's eyes are directed. On the other hand, when the XR/PCC objectis output on a display provided inside the self-driving vehicle, atleast a part of the XR/PCC object may be output to overlap an object onthe screen. For example, the self-driving vehicle 17200 may outputXR/PCC objects corresponding to objects such as a road, another vehicle,a traffic light, a traffic sign, a two-wheeled vehicle, a pedestrian,and a building.

The virtual reality (VR) technology, the augmented reality (AR)technology, the mixed reality (MR) technology and/or the point cloudcompression (PCC) technology according to the embodiments are applicableto various devices.

In other words, the VR technology is a display technology that providesonly CG images of real-world objects, backgrounds, and the like. On theother hand, the AR technology refers to a technology that shows avirtually created CG image on the image of a real object. The MRtechnology is similar to the AR technology described above in thatvirtual objects to be shown are mixed and combined with the real world.However, the MR technology differs from the AR technology in that the ARtechnology makes a clear distinction between a real object and a virtualobject created as a CG image and uses virtual objects as complementaryobjects for real objects, whereas the MR technology treats virtualobjects as objects having equivalent characteristics as real objects.More specifically, an example of MR technology applications is ahologram service.

Recently, the VR, AR, and MR technologies are sometimes referred to asextended reality (XR) technology rather than being clearly distinguishedfrom each other. Accordingly, embodiments of the present disclosure areapplicable to any of the VR, AR, MR, and XR technologies. Theencoding/decoding based on PCC, V-PCC, and G-PCC techniques isapplicable to such technologies.

The PCC method/device according to the embodiments may be applied to avehicle that provides a self-driving service.

A vehicle that provides the self-driving service is connected to a PCCdevice for wired/wireless communication.

When the point cloud compression data (PCC) transmission/receptiondevice according to the embodiments is connected to a vehicle forwired/wireless communication, the device may receive/process contentdata related to an AR/VR/PCC service, which may be provided togetherwith the self-driving service, and transmit the same to the vehicle. Inthe case where the PCC transmission/reception device is mounted on avehicle, the PCC transmission/reception device may receive/processcontent data related to the AR/VR/PCC service according to a user inputsignal input through a user interface device and provide the same to theuser. The vehicle or the user interface device according to theembodiments may receive a user input signal. The user input signalaccording to the embodiments may include a signal indicating theself-driving service.

Meanwhile, the point cloud video encoder on the transmitting side mayfurther perform a spatial partitioning process of spatially partitioning(or dividing) the point cloud data into one or more 3D blocks beforeencoding the point cloud data. That is, in order for the encoding andtransmission operations of the transmission device and the decoding andrendering operations of the reception device to be performed in realtime and processed with low latency, the transmission device mayspatially partition the point cloud data into a plurality of regions. Inaddition, the transmission device may independently or non-independentlyencode the spatially partitioned regions (or blocks), thereby enablingrandom access and parallel encoding in the three-dimensional spaceoccupied by the point cloud data. In addition, the transmission deviceand the reception device may perform encoding and decoding independentlyor non-independently for each spatially partitioned region (or block),thereby preventing errors from being accumulated in the encoding anddecoding process.

FIG. 15 is a diagram illustrating another example of a point cloudtransmission device according to embodiments, including a spatialpartitioner.

The point cloud transmission device according to the embodiments mayinclude a data input unit 51001, a coordinates transformation unit51002, a quantization processor 51003, a spatial partitioner 51004, asignaling processor 51005, a geometry encoder 51006, an attributeencoder 51007, and a transmission processor 51008. According toembodiments, the coordinates transformation unit 51002, the quantizationprocessor 51003, the spatial partitioner 51004, the geometry encoder51006, and the attribute encoder 51007 may be referred to as point cloudvideo encoders.

The data input unit 51001 may perform some or all of the operations ofthe point cloud video acquisition unit 10001 of FIG. 1 , or may performsome or all of the operations of the data input unit 12000 of FIG. 12 .The coordinates transformation unit 51002 may perform some or all of theoperations of the coordinates transformation unit 40000 of FIG. 4 .Further, the quantization processor 51003 may perform some or all of theoperations of the quantization unit 40001 of FIG. 4 , or may performsome or all of the operations of the quantization processor 12001 ofFIG. 12 .

The spatial partitioner 51004 may spatially partition the point clouddata quantized and output from the quantization processor 51003 into oneor more 3D blocks based on a bounding box and/or a sub-bounding box.Here, the 3D block may refer to a tile group, a tile, a slice, a codingunit (CU), a prediction unit (PU), or a transform unit (TU). In oneembodiment, the signaling information for spatial partition isentropy-encoded by the signaling processor 51005 and then transmittedthrough the transmission processor 51008 in the form of a bitstream.

FIGS. 16A to 16C illustrate an embodiment of partitioning a bounding boxinto one or more tiles. As shown in FIG. 16A, a point cloud object,which corresponds to point cloud data, may be expressed in the form of abox based on a coordinate system, which is referred to as a boundingbox. In other words, the bounding box represents a cube capable ofcontaining all points of the point cloud.

FIG. 16B and FIG. 16C illustrate an example in which the bounding box ofFIG. 16A is partitioned into tile 1# and tile 2#, and tile 2# ispartitioned again into slice 1# and slice 2#.

In one embodiment, the point cloud content may be one person such as anactor, multiple people, one object, or multiple objects. In a largerrange, it may be a map for autonomous driving or a map for indoornavigation of a robot. In this case, the point cloud content may be avast amount of locally connected data. In this case, the point cloudcontent cannot be encoded/decoded at once, and accordingly tilepartitioning may be performed before the point cloud content iscompressed. For example, room #101 in a building may be partitioned intoone tile and room #102 in the building may be partitioned into anothertile. In order to support fast encoding/decoding by applyingparallelization to the partitioned tiles, the tiles may be partitioned(or split) into slices again. This operation may be referred to as slicepartitioning (or splitting).

That is, a tile may represent a partial region (e.g., a rectangularcube) of a 3D space occupied by point cloud data according toembodiments. According to embodiments, a tile may include one or moreslices. The tile according to the embodiments may be partitioned intoone or more slices, and thus the point cloud video encoder may encodepoint cloud data in parallel.

A slice may represent a unit of data (or bitstream) that may beindependently encoded by the point cloud video encoder according to theembodiments and/or a unit of data (or bitstream) that may beindependently decoded by the point cloud video decoder. A slice may be aset of data in a 3D space occupied by point cloud data, or a set of somedata among the point cloud data. A slice according to the embodimentsmay represent a region or set of points included in a tile according toembodiments. According to embodiments, a tile may be partitioned intoone or more slices based on the number of points included in one tile.For example, one tile may be a set of points partitioned by the numberof points. According to embodiments, a tile may be partitioned into oneor more slices based on the number of points, and some data may be splitor merged in the partitioning process. That is, a slice may be a unitthat may be independently coded within a corresponding tile. In thisway, a tile obtained by spatially partitioning may be partitioned intoone or more slices for fast and efficient processing.

The point cloud video encoder according to the embodiments may encodepoint cloud data on a slice-by-slice basis or a tile-by-tile basis,wherein a tile includes one or more slices. In addition, the point cloudvideo encoder according to the embodiments may perform differentquantization and/or transformation for each tile or each slice.

Positions of one or more 3D blocks (e.g., slices) spatially partitionedby the spatial partitioner 51004 are output to the geometry encoder51006, and the attribute information (or attributes) is output to theattribute encoder 51007. The positions may be position information aboutthe points included in a partitioned unit (box, block, tile, tile group,or slice), and are referred to as geometry information.

The geometry encoder 51006 constructs and encodes (i.e., compresses) anoctree based on the positions output from the spatial partitioner 51004to output a geometry bitstream. The geometry encoder 51006 mayreconstruct an octree and/or an approximated octree and output the sameto the attribute encoder 51007. The reconstructed octree may be referredto as reconstructed geometry (or restored geometry).

The attribute encoder 51007 encodes (i.e., compresses) the attributesoutput from the spatial partitioner 51004 based on the reconstructedgeometry output from the geometry encoder 51006, and outputs anattribute bitstream.

FIG. 17 is a detailed block diagram illustrating another example of thegeometry encoder 51006 and the attribute encoder 51007 according toembodiments.

The voxelization processor 53001, the octree generator 53002, thegeometry information predictor 53003, and the arithmetic coder 53004 ofthe geometry encoder 51006 of FIG. 17 may perform some or all of theoperations of the octree analysis unit 40002, the surface approximationanalysis unit 40003, the arithmetic encoder 40004, and the geometryreconstruction unit 40005 of FIG. 4 , or may perform some or all of theoperations of the voxelization processor 12002, the octree occupancycode generator 12003, and the surface model processor 12004, theintra/inter-coding processor 12005, and the arithmetic coder 12006 ofFIG.

The attribute encoder 51007 of FIG. 17 may include a colortransformation processor 53005, an attribute transformation processor5306, a LOD configuration unit 53007, a neighbor point groupconfiguration unit 53008, an attribute information prediction unit53009, a residual attribute information quantization processor 53010,and an arithmetic coder 53011.

In an embodiment, a quantization processor may be further providedbetween the spatial partitioner 51004 and the voxelization processor53001. The quantization processor quantizes positions of one or more 3Dblocks (e.g., slices) spatially partitioned by the spatial partitioner51004. In this case, the quantization processor may perform some or allof the operations of the quantization unit 40001 of FIG. 4 , or performsome or all of the operations of the quantization processor 12001 ofFIG. 12 . When the quantization processor is further provided betweenthe spatial partitioner 51004 and the voxelization processor 53001, thequantization processor 51003 of FIG. 15 may or may not be omitted.

The voxelization processor 53001 according to the embodiments performsvoxelization based on the positions of the one or more spatiallypartitioned 3D blocks (e.g., slices) or the quantized positions thereof.Voxelization refers to the minimum unit expressing position informationin a 3D space. Points of point cloud content (or 3D point cloud video)according to embodiments may be included in one or more voxels.According to embodiments, one voxel may include one or more points. Inan embodiment, in the case where quantization is performed beforevoxelization is performed, a plurality of points may belong to onevoxel.

In the present specification, when two or more points are included inone voxel, the two or more points are referred to as duplicated points.That is, in the geometry encoding process, overlapping points may begenerated through geometry quantization and voxelization.

The voxelization processor 53001 according to the embodiments may outputthe duplicated points belonging to one voxel to the octree generator53002 without merging the same, or may merge the multiple points intoone point and output the merged point to the octree generator 53002.

The octree generator 53002 according to the embodiments generates anoctree based on a voxel output from the voxelization processor 53001.

The geometry information predictor 53003 according to the embodimentspredicts and compresses geometry information based on the octreegenerated by the octree generator 53002, and outputs the predicted andcompressed information to the arithmetic coder 53004. In addition, thegeometry information predictor 53003 reconstructs the geometry based onthe positions changed through compression, and outputs the reconstructed(or decoded) geometry to the LOD configuration unit 53007 of theattribute encoder 51007. The reconstruction of the geometry informationmay be performed in a device or component separate from the geometryinformation predictor 53003. In another embodiment, the reconstructedgeometry may also be provided to the attribute transformation processor53006 of the attribute encoder 51007.

The color transformation processor 53005 of the attribute encoder 51007corresponds to the color transformation unit 40006 of FIG. 4 or thecolor transformation processor 12008 of FIG. 12 . The colortransformation processor 53005 according to the embodiments performscolor transformation coding of transforming color values (or textures)included in the attributes provided from the data input unit 51001and/or the spatial partitioner 51004. For example, the colortransformation processor 53005 may transform the format of colorinformation (e.g., from RGB to YCbCr). The operation of the colortransformation processor 53005 according to the embodiments may beoptionally applied according to color values included in the attributes.In another embodiment, the color transformation processor 53005 mayperform color transformation coding based on the reconstructed geometry.For details of the geometry reconstruction, refer to the description ofFIGS. 1 to 9 .

The attribute transformation processor 53006 according to theembodiments may perform attribute transformation of transformingattributes based on positions on which geometry encoding has not beenperformed and/or the reconstructed geometry.

The attribute transformation processor 53006 may be referred to as arecoloring unit.

The operation of the attribute transformation processor 53006 accordingto the embodiments may be optionally applied according to whetherduplicated points are merged. According to an embodiment, merging of theduplicated points may be performed by the voxelization processor 53001or the octree generator 53002 of the geometry encoder 51006.

In the present specification, when points belonging to one voxel aremerged into one point in the voxelization processor 53001 or the octreegenerator 53002, the attribute transformation processor 53006 performsan attribute transformation. Take an example.

The attribute transformation processor 53006 performs an operationand/or method identical or similar to the operation and/or method of theattribute transformation unit 40007 of FIG. 4 or the attributetransformation processor 12009 of FIG. 12 .

According to embodiments, the geometry information reconstructed by thegeometry information predictor 53003 and the attribute informationoutput from the attribute transformation processor 53006 are provided tothe LOD configuration unit 53007 for attribute compression.

According to embodiments, the attribute information output from theattribute transformation processor 53006 may be compressed by one or acombination of two or more of RAHT coding, LOD-based predictivetransform coding, and lifting transform coding based on thereconstructed geometry information.

Hereinafter, it is assumed that attribute compression is performed byone or a combination of the LOD-based predictive transform coding andthe lifting transform coding as an embodiment. Thus, a description ofthe RAHT coding will be omitted. For details of the RAHT transformcoding, refer to the descriptions of FIGS. 1 to 9 .

The LOD configuration unit 53007 according to the embodiments generatesa Level of Detail (LOD).

The LOD is a degree representing the detail of the point cloud content.As the LOD value decreases, it indicates that the detail of the pointcloud content is degraded. As the LOD value increases, it indicates thatthe detail of the point cloud content is enhanced. Points of thereconstructed geometry (i.e., the reconstructed positions) may beclassified according to the LOD.

In an embodiment, in the predictive transform coding and the liftingtransform coding, points may be divided into LODs and grouped.

This operation may be referred to as an LOD generation process, and agroup having different LODs may be referred to as an LOD₁ set. Here, ldenotes the LOD and is an integer starting from 0. LOD₀ is a setconsisting of points with the largest distance therebetween. As lincreases, the distance between points belonging to LOD₁ decreases.

When the LOD₁ set is generated by the LOD configuration unit 53007, theneighbor point configuration unit 53008 according to the embodiment mayfind X (>0) nearest neighbor (NN) points in a group having the same orlower LOD (i.e., a large distance between nodes) based on the LOD₁ setand register the same in the predictor as a neighbor set. X is themaximum number of points that may be set as neighbors. X may be input asa user parameter, or be signaled in signaling information through thesignaling processor 51005 (e.g., the lifting_num_pred_nearest_neighboursfield signaled in the APS).

Referring to FIG. 9 as an example, neighbors of P3 belonging to LOD₁ arefound in LOD₀ and LOD₁. For example, when the maximum number (X) ofpoints that may be set as neighbors is 3, three nearest neighbor nodesof P3 may be P2 P4 P6. These three nodes are registered as in thepredictor of P3 as a neighbor set. In an embodiment, among theregistered nodes, the neighbor node P4 is the closest to P3 in terms ofdistance, then P6 is the next nearest node, and P2 is the farthest nodeamong the three nodes. Here, X=3 is merely an embodiment configured toprovide understanding of the present disclosure. The value of X mayvary.

As described above, all points of the point cloud data may have apredictor, respectively.

The attribute information prediction unit 53009 according to embodimentspredicts attribute values from neighbor points registered in a predictorand obtains a residual attribute value of a corresponding point based onthe predicted attribute values. The residual attribute value is outputto the residual attribute information quantization processor 53010.

Next, LOD generation and neighbor point search will be described indetail.

As described above, for attribute compression, the attribute encoderperforms a process of generating LODs based on points of reconstructedgeometry and searching for the nearest neighbor points of a point to beencoded based on the generated LODs. According to embodiments, even theattribute decoder of the reception device performs a process ofgenerating LODs and searching for the nearest neighbor points of a pointto be decoded based on the generated LODs.

According to embodiments, the LOD configuration unit 53007 may configureone or more LODs using one or more LOD generation methods (or LODconfiguration methods).

According to embodiments, an LOD generation method used in the LODconfiguration unit 53007 may be input as a user parameter or may besignaled in signaling information through the signaling processor 51005.For example, the LOD generation method may be signaled in an APS.

According to embodiments, the LOD generation methods may be classifiedinto an octree-based LOD generation method, a distance-based LODgeneration method, and a sampling-based LOD generation method.

FIG. 18 is a diagram illustrating an example of generating LODs based onan octree according to embodiments.

According to embodiments, when LODs are generated based on an octree,each depth level of the octree may be matched to each LOD as illustratedin FIG. 18 . That is, the octree-based LOD generation method is a methodof generating LODs using characteristics in which a detail representingpoint cloud data gradually increases as a depth level increases (i.e., adirection from a root to a leaf) in an octree structure. According toembodiments, an octree-based LOD configuration may be performed from aroot node to a leaf node or, conversely, from the leaf node to the rootnode.

The distance-based LOD generation method according to embodiments is amethod of arranging points using a Morton code and generating LODs basedon a distance between the points.

The sampling-based LOD generation method according to embodiments is amethod of arranging points using a Morton code and classifying everyk-th point as a lower LOD that may be a candidate of a neighbor set.That is, during sampling, every k-th point may be selected andclassified as a lower candidate set belonging to LOD₀ to LOD_(l-1), andthe remaining points may be registered in an LOD_(l) set. That is, theselected points become a candidate group that may be selected as aneighbor point set of the points registered in the LOD_(l) set. k maydiffer according to point cloud content. Here, the points may be pointsof a captured point cloud or points of reconstructed geometry.

In the present disclosure, a lower candidate set corresponding to LOD₀to LOD_(l-1) based on the LOD_(l) set will be referred to as a retainedset or an LOD retained set. LOD₀ is a set consisting of points with thelargest distance therebetween. As l increases, the distance betweenpoints belonging to LOD_(l) decreases.

According to embodiments, points in the LOD_(l) set are also arrangedbased on a Morton code order. In addition, points included in each ofthe LOD₀ to LOD_(l-1) sets are also arranged based on a Morton codeorder.

FIG. 19 is a diagram illustrating an example of arranging points of apoint cloud in a Morton code order according to embodiments.

That is, a Morton code of each point is generated based on x, y, and zposition values of each point of a point cloud. When Morton codes ofpoints of the point cloud are generated through this process, the pointsof the point cloud may be arranged in a Morton code order. According toembodiments, the points of the point cloud may be arranged in ascendingorder of the Morton codes. The order of points arranged in ascendingorder of the Morton codes may be referred to as a Morton order.

The LOD configuration unit 53007 according to embodiments may generateLODs by performing sampling upon points arranged in Morton order.

The LOD configuration unit 53007 generates the LOD_(l) set by applyingat least one of the octree-based LOD generation method, thedistance-based LOD generation method, or the sampling-based LODgeneration method, the neighbor point set configuration unit 53008 maysearch for X(>0) nearest neighbor (NN) points in a group having the sameor lower LOD (i.e. a large distance between nodes) based on the LOD_(l)set and register the X NN points as a neighbor point set in a predictor.

In this case, since much time is consumed to search for all points inorder to configure the neighbor point set, an embodiment of searchingfor neighbor points within a neighbor point search range including onlypartial points is provided. The search range refers to the number ofpoints and may be 128, 256, or another value. According to embodiments,information about the search range may be set in the neighbor point setconfiguration unit 53008 or may be input as the user parameter.Alternatively, the information about the search range may be signaled insignaling information through the signaling processor 51005 (e.g. alifting_search_range field signaled in the APS).

FIG. 20 is a diagram illustrating an example of searching for neighborpoints in a search range based on LOD according to embodiments. Arrowsillustrated in the drawing indicate a Morton order according toembodiments.

In FIG. 20 , in an embodiment, an index list includes an LOD set (i.e.,LOD_(l)) to which points to be encoded belong and a retained listincludes at least one lower LOD set (e.g., LOD₀ to LOD_(l-1)) based onthe LOD_(l) set.

According to embodiments, the points of the index list are arranged inascending order based on the size of a Morton code, and the points ofthe retained list are also arranged in ascending order based on the sizeof the Morton code. Therefore, the foremost point of the points arrangedin Morton order in the index list and retained list has the smallestsize of the Morton code.

The neighbor point set configuration unit 53008 according to embodimentsmay search for a point having a Morton code nearest to a Morton code ofa point Px among points located before the point Px in order (i.e.,points having Morton codes smaller than or equal to the Morton code ofthe point Px) among points belonging to LOD₀ to LOD_(l-1) sets and/orpoints belonging to the LOD_(l) set, in order to generate a neighborpoint set of the point Px (i.e., a point to be encoded or a currentpoint) belonging to the LOD_(l) set. In the present disclosure, thesearched point will be referred to as Pi or a center point.

According to embodiments, when searching for the center point Pi, theneighbor point set configuration unit 53008 may search for the point Pihaving a Morton code nearest to a Morton code of the point Px among allpoints located before the point Px or search for the point Pi having theMorton code nearest to the Morton code of the point Px among points in asearch range. In the present disclosure, the search range may beconfigured by the neighbor point set configuration unit 53008 or may beinput as a user parameter. In addition, information about the searchrange may be signaled in signaling information through the signalingprocessor 51005. The present disclosure provides an embodiment ofsearching for the center point Pi in the retained list when the numberof LODs is plural and searching for the central point Pi in the indexlist when the number of LODs is one. For example, when the number ofLODs is 1, the search range may be determined based on the position of acurrent point in a list arranged with Morton codes.

The neighbor point set configuration unit 53008 according to embodimentscompares distance values between the point Px and points belonging to aneighbor point search range located before the searched (or selected)center point Pi (i.e., on the left side of the center point in FIG. 20 )and after the searched (or selected) center point Pi (i.e., on the rightside of the center point in FIG. 20 ). The neighbor point setconfiguration unit 53008 may register X (e.g., 3) NN points as aneighbor point set. In an embodiment, the neighbor point search range isthe number of points. The neighbor point search range according toembodiments may include one or more points located before (i.e., infront of) and/or after (i.e., behind) the center point Pi in Mortonorder. X is the maximum number of points that may be registered asneighbor points.

According to embodiments, information about the neighbor point searchrange and information about the maximum number X of points that may beregistered as neighbor points may be configured by the neighbor pointset configuration unit 53008 or may be input as a user parameter orsignaled in signaling information through the signaling processor 51005(e.g., a lifting_search_range field and alifting_num_pred_nearest_neighbours field signaled in an APS). Accordingto embodiments, an actual search range may be a value obtained bymultiplying the value of the lifting_search_range field by 2 and thenadding the center point to the resultant value (i.e., (value of thelifting_search_range field*2)+center point), a value obtained by addingthe center value to the value of the lifting_search_range field (i.e.,value of lifting_search_range field+center point), or the value of thelifting_search_range field. The present disclosure provides anembodiment of searching for neighbor points of the point Px in theretained list when the number of LODs is plural and searching forneighbor points of the point Px in the index list when the number ofLODs is one.

For example, if the number of LODs is 2 or more and the informationabout the search range (e.g., lifting_search_range field) is 128, theactual search range includes 128 points before the center point Pi, 128points after the center point Pi, and the center point Pi in theretained list arranged with Morton codes. As another example, if thenumber of LODs is 1 and the information about the search range is 128,the actual search range includes 128 points before the center point Piand the center point Pi in the index list arranged with Morton codes. Asanother example, if the number of LODs is 1 and the information aboutthe search range is 128, the actual search range includes 128 pointsbefore the current point Px in the index list arranged with Mortoncodes.

According to embodiments, the neighbor point set configuration unit53008 may compare distance values between points within the actualsearch range and the point Px and register X NN points as a neighborpoint set of the point Px.

On the other hand, attribute characteristics of content may appeardifferently depending on an object from which content is captured, acaptured scheme, or equipment used for capturing content.

FIGS. 21A and 21B are diagrams illustrating examples of point cloudcontent according to embodiments.

FIG. 21A is an example of dense point cloud data acquired by capturingone object with a 3D scanner and an attribute correlation of the objectmay be high. FIG. 21B is an example of sparse point cloud data acquiredby capturing a wide area with a LiDAR device and an attributecorrelation between points may be low. An example of the dense pointcloud data may be a still image and an example of the sparse point clouddata may be an image captured by a drone or a self-driving vehicle.

In this way, attribute characteristics of content may appear differentlydepending on an object from which content is captured, a capturedscheme, or equipment used for capturing content. If neighbor pointsearch is performed to configure a neighbor point set when encodingattributes without taking different attribute characteristics intoaccount, distances between a corresponding point and registered neighborpoints may not all be adjacent. For example, when X (e.g., 3) NN pointsare registered as the neighbor point set after comparing distance valuesbetween the point Px and points belonging to the neighbor point searchrange before and after the center point Pi, at least one of the Xregistered neighbor points may be far away from the point Px. In otherwords, at least one of the X registered neighbor points may not be anactual neighbor point of the point Px.

This phenomenon is more likely to occur in the sparse point cloud datathan in the dense point cloud data. For example, as illustrated in FIG.21A, if there is one object in content, the one object has colorcontinuity/similarity and is densely captured, and a captured area issmall, there may be content having a high attribute correlation betweenpoints even when the points are a little apart from a neighbor point.Conversely, as illustrated in FIG. 21B, if a considerably wide area issparsely captured with a LiDAR device, there may be content having acolor correlation only when the distance to the neighbor point is withina specific range. Therefore, when encoding attributes of the sparsepoint cloud data as illustrated in FIG. 21B, some of the neighbor pointsregistered through neighbor point search may not be actual neighborpoints of the point to be encoded.

This phenomenon has a high probability of occurring when points arearranged in Morton order when generating LOD. That is, although theMorton order quickly arranges neighbor points, a jumping section (i.e.,intermittent long lines in FIG. 19 ) occurs due to a zigzag scan featureof the Morton code and then locations of points may be greatly changed.In this case, some of the neighbor points registered through theneighbor point search within the search range may not be the actual NNpoints of the point to be encoded.

FIGS. 22A and 22B are diagrams illustrating examples of an averagedistance, a minimum distance, and a maximum distance of points belongingto a neighbor point set according to embodiments.

FIG. 22A illustrates an example of the 100th frame of content calledFORD and FIG. 22B illustrates an example of the first frame of contentcalled QNX.

FIGS. 22A and 22B are exemplary embodiments to aid in understandingthose skilled in the art. FIGS. 22A and 22B may be different frames ofthe same content or specific frames of different content.

For convenience of explanation, the frame illustrated in FIG. 22A isreferred to as a first frame and the frame illustrated in FIG. 22B isreferred to as a second frame.

In the first frame of FIG. 22A, the total number of points is 80265,there are 4 points in which only one NN point is registered, there are60 points in which 2 NN points are registered, and there are 80201points in which 3 NN points are registered. That is, the maximum numberof points that may be registered as the NN points is 3 but may be 1 or 2depending on positions of points in the frame.

In the second frame of FIG. 22B, the total number of points is 31279,there are 3496 points in which only one NN is registered, there are 219points in which 2 NN points are registered, and there are 27564 pointsare registered in which 3 NN points are registered.

In the first frame of FIG. 22A and the second frame of FIG. 22B, NN1 isthe NN point, NN2 is the next-NN point, and NN3 is the third NN point.The maximum distance of NN2 in the first frame of FIG. 22A is 1407616and the maximum distance of NN3 is 1892992, whereas the maximum distanceof NN2 in the second frame of FIG. 22B is 116007040 and the maximumdistance of NN3 is 310887696.

That is, it may be appreciated that there is a considerable differencein distance according to content or frame. Accordingly, some of theregistered neighbor points may not be actual neighbor points.

In this way, if the distance difference between a point to be encodedand a registered neighbor point becomes large, an attribute differencebetween the two points is likely to be large. In addition, whenprediction is performed based on the neighbor point set including theseneighbor points and a residual attribute value is obtained, the residualattribute value may increase, resulting in an increase in a bitstreamsize.

In other words, characteristics of content or a frame may have an effecton configuration of the neighbor point set for attribute prediction andthis may affect compression efficiency of attributes.

Therefore, in this disclosure, when configuring the neighbor point setin order to encode attributes of point cloud content, neighbor pointsare selected in consideration of an attribute correlation between pointsof the content, so that meaningless points are not selected as theneighbor point set. Then, since a residual attribute value decreases andthe bitstream size is thereby reduced, compression efficiency ofattributes is improved. In other words, the present disclosure improvescompression efficiency of attributes using a method of restrictingpoints that may be selected as the neighbor point set in considerationof the attribute correlation between points of content.

The present disclosure provides an embodiment of applying a maximumdistance of a neighbor point (also referred to as a maximum NN distance)in order to consider an attribute correlation between points of contentwhen configuring a neighbor point set in an attribute encoding processof point cloud content.

The present disclosure provides an embodiment of configuring theneighbor point set by applying a search range and/or a maximum NNdistance when encoding attributes of point cloud content.

For example, as a result of comparing distance values between a point Pxand points belonging to a neighbor point search range before and after acenter point Pi, points farther than the maximum NN distance among X(e.g., 3) NN points are not registered in the neighbor point set of thepoint Px. In other words, only points within the maximum NN distanceamong X (e.g., 3) NN points are registered in the neighbor point set ofthe point Px as a result of comparing distance values between the pointPx and points belonging to the neighbor point search range before andafter the center point Pi. For example, if one of the three pointshaving the nearest distance is farther than the maximum NN distance, thefarther-away point may be excluded and only the remaining two points maybe registered in the neighbor point set of the point Px.

The neighbor point set configuration unit 53008 according to embodimentsmay apply the maximum NN distance according to attribute characteristicsof content in order to achieve optimum compression efficiency ofattributes regardless of the attribute characteristics of content. Here,the maximum NN distance may be calculated differently according to theLOD generation method (the octree-based, distance-based, orsampling-based LOD generation method). That is, the maximum NN distancemay be applied differently to each LOD.

In the present disclosure, the maximum NN distance is usedinterchangeably with a maximum distance of a neighbor point or a maximumneighbor point distance. In other words, in the present disclosure, themaximum NN distance, the maximum distance of the neighbor point, and themaximum neighbor point distance have the same meaning.

In the present disclosure, the maximum NN distance may be obtained bymultiplying a base neighbor point distance (referred to as a basedistance or a reference distance) by NN_range as illustrated in Equation5 below.Maximum Neighbor Point Distance=Base Neighbor PointDistance*NN_range  Equation 5

In Equation 5, NN_range is a range within which a neighbor point may beselected and may be referred to as a maximum neighbor point range, aneighbor point range, or an NN_range.

The neighbor point set configuration unit 53008 according to embodimentsmay set NN_range automatically or manually according to characteristicsof content or set NN_range through input as a user parameter. Inaddition, information about NN_range may be signaled in signalinginformation through the signaling processor 51005. The signalinginformation including the information about NN_range may be at least oneof an SPS, an APS, a tile parameter set, or an attribute slice header.The attribute decoder of the reception device may generate the neighborpoint set based on the signaling information. In this case, neighborpoints that have been used in attribute prediction of attribute encodingmay be restored and applied to attribute decoding.

According to embodiments, the neighbor point set configuration unit53008 may calculate/configure the base neighbor point distance bycombining one or more of an octree-based method, a distance-basedmethod, a sampling-based method, an average difference-based method ofMorton codes for each LOD, and an average distance difference-basedscheme for each LOD. According to embodiments, the method ofcalculating/configuring the base neighbor point distance may be signaledin signaling information (e.g., APS) through the signaling processor51005.

Next, embodiments of acquiring a base distance of a neighbor point and amaximum distance of a neighbor point when LODs are generated based on anoctree| will be described.

As illustrated in FIG. 18 , when the LOD configuration unit 53007generates octree-based LODs, each depth level of the octree may bematched to each LOD.

The neighbor point set configuration unit 53008 according to embodimentsmay obtain a maximum distance of a neighbor point based on theoctree-based LODs.

According to embodiments, when the LOD configuration unit 53007generates the octree-based LODs, spatial scalability may be supported.With spatial scalability, a lower resolution point cloud, like athumbnail with less decoder complexity and/or less bandwidth, may beaccessed when a source point cloud is dense. For example, a spatialscalability function of geometry may be provided by a process ofencoding or decoding occupancy bits only up to a selected depth level byadjusting a depth level of the octree during encoding/decoding ofgeometry. In addition, even during encoding/decoding of an attribute,the spatial scalability function of the attribute may be provided by aprocess of generating LODs from a selected depth level of the octree andconfiguring points on which encoding/decoding of the attribute is to beperformed.

According to embodiments, for spatial scalability, the LOD configurationunit 53007 may generate the octree-based LODs and the neighbor point setconfiguration unit 53008 may perform neighbor point search based on thegenerated octree-based LODs.

When the neighbor point set configuration unit 53008 according toembodiments searches for neighbor points in previous LODs based onpoints belonging to a current octree-based generated LOD, a maximumneighbor point distance may be obtained by multiplying a base neighborpoint distance (referred to as a base distance or a reference distance)by NN_range (i.e., base neighbor point distance*NN_range).

According to embodiments, a maximum distance (i.e., a diagonal distance)of one node in a specific octree-based generated LOD may be defined asthe base distance (or a base neighbor point distance).

According to embodiments, when searching for neighbor points in previousLODs (e.g., LOD₀ to LOD_(l-1) in the retained list) based on pointsbelonging to a current LOD (e.g., LOD_(l) set in the index list), adiagonal distance of a higher node (parent node) of an octree node ofthe current LOD may be a base distance for acquiring the maximumneighbor point distance.

According to an embodiment, the base distance, which is the diagonaldistance (i.e., the maximum distance) of one node at a specific LOD, maybe obtained as in Equation 6 below.Base distance=√{square root over (2^(lod) ² ×3)}  Equation 6

According to another embodiment, the base distance, which is thediagonal distance (i.e., the maximum distance) of one node at a specificLOD, may be obtained based on L2 as shown in Equation 7 below.L2-based base distance=2^(lod) ² ×3  Equation 7

In general, when calculating the distance between two points, theManhattan distance calculation method or the Euclidean distancecalculation method is used. The Manhattan distance will be referred toas an L1 distance and the Euclidean distance will be referred to as anL2 distance. A Euclidean space may be defined using the Euclideandistance and a norm corresponding to this distance will be referred toas a Euclidean norm or L2 norm. Norm is a method (function) of measuringthe length or size of a vector.

FIG. 23A illustrates an example of acquiring a diagonal distance of anoctree node of LOD₀. According to embodiments, when the example of FIG.23A is applied to Equation 6, the base distance becomes √{square rootover (3)} and, when the example of FIG. 23A is applied to Equation 7,the base distance becomes 3.

FIG. 23B illustrates an example of calculating a diagonal distance of anoctree node of LOD₁. According to embodiments, when the example of FIG.23B is applied to Equation 6, the base distance becomes √{square rootover (12)} and, when the example of FIG. 23B is applied to Equation 7,the base distance becomes 12.

FIGS. 24A and 24B illustrate examples of a range NN_range that may beselected as a neighbor point at each LOD. More specifically, FIG. 24Aillustrates an example when NN_range is 1 at LOD₀ and FIG. 24Billustrates an example when NN_range is 3 at LOD₀. According toembodiments, NN_range may be set automatically or manually according tocharacteristics of content or may be set through input as a userparameter. Information about NN_range may be signaled in signalinginformation. The signaling information including the information aboutNN_range may be at least one of an SPS, an APS, a tile parameter set, oran attribute slice header. According to embodiments, the range NN_rangethat may be set as a neighbor point may be set to an arbitrary value(e.g., a multiple of the base distance) regardless of an octree noderange.

According to embodiments, when the example of FIG. 24A is applied toEquations 5 and 6, the maximum neighbor point distance becomes √{squareroot over (3)}(=√{square root over (3)}*1) and, when the example of FIG.24B is applied to Equation 5 and Equation 6, the maximum neighbor pointdistance becomes √{square root over (3)}(=√{square root over (3)}*3).

According to embodiments, when the example of FIG. 24A is applied toEquation 5 and Equation 7, the maximum neighbor point distance becomes 3(=3*1) and, when the example of FIG. 24B is applied to Equation 5 andEquation 7, the maximum neighbor point distance is 9 (=3*3).

Next, embodiments of acquiring the base neighbor point distance and themaximum neighbor point distance when distance-based LODs are generatedwill be described.

According to embodiments, when configuring the distance-based LODs, thebase neighbor point distance may be set as a distance used to configurepoints at a current LOD (i.e., LOD_(l)). That is, the base distance foreach LOD may be set to a distance (dist2_(L)) applied to LOD generationat an L level of an LOD.

FIG. 25 illustrates examples of a base neighbor point distance belongingto each LOD according to embodiments.

Therefore, when configuring distance-based LODs, the maximum neighborpoint distance may be obtained by multiplying a distance-based baseneighbor point distance dist2_(L) by NN_range.

According to embodiments, NN_range may be set automatically or manuallyaccording to characteristics of content by the neighbor point setconfiguration unit 53008 or may be set through input as a userparameter. Information about NN_range may be signaled in signalinginformation. The signaling information including the information aboutNN_range may be at least one of an SPS, an APS, a tile parameter set, oran attribute slice header. According to embodiments, NN_range that maybe set as a neighbor point may be set to an arbitrary value (e.g., amultiple of the base distance). According to embodiments, NN_Range maybe adjusted according to a method of calculating a distance to theneighbor point.

Next, embodiments of acquiring a base neighbor point distance and amaximum neighbor point distance when sampling-based LODs are generatedwill be described.

According to embodiments, when sampling-based (i.e., decimation-based)LODs are generated, the base distance may be determined according to asampling rate. According to embodiments, in a sampling-based LODconfiguration method, Morton codes are generated based on positionvalues of points, the points are arranged according to the Morton codes,and then points that do not correspond to the k-th point according tothe arranged order are registered in a current LOD. Therefore, each LODmay have a different k and may be expressed as k_(L).

According to an embodiment, when LODs are generated by applying asampling method to points arranged based on Morton codes, an averagedistance of consecutive k_(L)-th points at each LOD may be set to thebase neighbor point distance at each LOD. That is, the average distanceof consecutive k_(L)-th points at a current LOD arranged based on theMorton codes may be set to the base neighbor point distance of thecurrent LOD. According to another embodiment, when LODs are generated byapplying the sampling method to points arranged based on the Mortoncodes, a depth level of an octree may be estimated from the current LODlevel, and a diagonal length of the node of the estimated octree depthlevel may be set to the base neighbor point distance. That is, the baseneighbor point distance may be obtained by applying Equation 6 or 7described above.

Therefore, when configuring sampling-based LODs, the maximum neighborpoint distance may be obtained by multiplying the sampling-based baseneighbor point distance by NN_range.

According to embodiments, NN_range may be automatically or manually setaccording to characteristics of content by the neighbor point setconfiguration unit 53008 or may be set through input as a userparameter. Information about NN_range may be signaled in signalinginformation. The signaling information including the information aboutNN_range may be at least one of an SPS, an APS, a tile parameter set, oran attribute slice header. According to embodiments, NN_range that maybe set as a neighbor point may be set to an arbitrary value (e.g., amultiple of the base distance). According to embodiments, NN_Range maybe adjusted according to a method of calculating a distance to theneighbor point.

In the present disclosure, the base neighbor point distance may beobtained using a method other than the above-described method ofacquiring the base neighbor point distance.

For example, when configuring the LODs, the base neighbor point distancemay be configured by calculating an average difference of Morton codesof sampled points. In addition, the maximum neighbor point distance maybe obtained by multiplying the configured base neighbor point distanceby NN_range.

As another example, the base neighbor point distance may be configuredby calculating an average distance difference for currently configuredLODs regardless of whether the distance-based method is used or thesampling-based method is used. In addition, the maximum neighbor pointdistance may be obtained by multiplying the configured base neighborpoint distance by NN_range.

As another example, the base neighbor point distance for each LOD may bereceived as the user parameter and then applied when acquiring themaximum neighbor point distance. Thereafter, information about thereceived base neighbor point distance may be signaled in signalinginformation.

As described above, the base neighbor point distance may be obtained byapplying the best base neighbor point distance calculation methodaccording to the LOD configuration method. In another embodiment, themethod of calculating the base neighbor point distance may be applied byselecting an arbitrarily desired method and signaling the selectedmethod in the signaling information so as to use the method to restorepoints in the decoder of the reception device. According to embodiments,a method of calculating the maximum neighbor point distance that issuitable for an attribute of point cloud content and is capable ofmaximizing compression performance may be used.

The LOD configuration method, the base neighbor point distanceacquisition method, the NN_range acquisition method, and the maximumneighbor point distance acquisition method, described above, may beapplied in the same or similar manner when configuring an LOD or aneighbor point set for prediction transformation or liftingtransformation.

According to an embodiment, if the LOD_(l) set is generated and themaximum neighbor point distance is determined as described above, theneighbor point set configuration unit 53008 searches for X (e.g., 3) NNpoints among points within a search range in a group having the same orlower LOD (i.e., a large distance between nodes) based on the LOD_(l)set. Then the neighbor point set configuration unit 53008 may registeronly NN points within the maximum neighbor point distance among the X(e.g., 3) NN points as a neighbor point set. Therefore, the number of NNpoints registered as the neighbor point set is equal to or smaller thanX. In other words, NN points not within the maximum neighbor pointdistance among the X NN points are not registered as the neighbor pointset and are excluded from the neighbor point set. For example, if two ofthe three NN points are not within the maximum neighbor point distance,the two NN points are excluded and only the other NN point, i.e., one NNpoint, is registered as the neighbor point set.

According to another embodiment, if the LOD_(l) set is generated and themaximum neighbor point distance is determined as described above, theneighbor point set configuration unit 53008 may search for X (e.g., 3)NN points within the maximum neighbor point distance among points withinthe search range in a group having the same or lower LOD (i.e., a largedistance between nodes) based on the LOD_(l) set and register the X NNpoints as the neighbor point set.

That is, the number of NN points that may be registered as the neighborpoint set may vary according to a timing (or position) of applying themaximum neighbor point distance. In other words, the number of NN pointsregistered as the neighbor point set may differ according to whether themaximum neighbor point distance is applied after searching for X NNpoints through neighbor point search or the maximum neighbor pointdistance is applied when calculating a distance between points. X is themaximum number that may be set as a neighbor point and may be input as auser parameter or signaled in signaling information through thesignaling processor 51005 (e.g., the lifting_num_pred_nearest_neighboursfield signaled in the APS).

FIG. 26 is a diagram illustrating an example of searching for neighborpoints by applying a search range and a maximum neighbor point distanceaccording to embodiments. Arrows illustrated in the drawing indicate aMorton order according to embodiments.

In FIG. 26 , in an embodiment, an index list includes an LOD set (i.e.,LOD_(l)) to which points to be encoded belong and a retained listincludes at least one lower LOD set (e.g., LOD₀ to LOD_(l-1)) based onan LOD_(l) set.

According to embodiments, points in the index list and points in theretained list are arranged in ascending order based on the size of aMorton code. Therefore, the foremost point of the points arranged inMorton order in the index list and the retained list has the smallestsize of the Morton code.

The neighbor point set configuration unit 53008 according to embodimentsmay search for a center point Pi having a Morton code nearest to aMorton code of a point Px among points located before the point Px inorder (i.e., points having Morton codes smaller than or equal to theMorton code of the point Px) among points belonging to LOD₀ to LOD_(l-1)sets and/or points belonging to the LOD_(l) set, in order to register aneighbor point set of the point Px (i.e., a point to be encoded or acurrent point) belonging to the LOD_(l) set.

According to embodiments, when searching for the center point Pi, theneighbor point set configuration unit 53008 may search for the point Pihaving the Morton code nearest to the Morton code of the point Px amongall points located before the point Px or search for the point Pi havingthe Morton code nearest to the Morton code of the point Px among pointsin a search range. In the present disclosure, the search range may beconfigured by the neighbor point set configuration unit 53008 or may beinput as a user parameter. In addition, information about the searchrange may be signaled in signaling information through the signalingprocessor 51005. The present disclosure provides an embodiment ofsearching for the center point Pi in the retained list when the numberof LODs is plural and searching for the central point Pi in the indexlist when the number of LODs is one. For example, when the number ofLODs is 1, the search range may be determined based on the position of acurrent point in a list arranged with Morton codes.

The neighbor point set configuration unit 53008 according to embodimentscompares distance values between the point Px and points belonging to aneighbor point search range located before the searched (or selected)center point Pi (i.e., on the left side of the center point in FIG. 26 )and after the searched (or selected) center point Pi (i.e., on the rightside of the center point in FIG. 26 ). The neighbor point setconfiguration unit 53008 may select X (e.g., 3) NN points and registeronly points within the maximum neighbor point distance at an LOD towhich the point Px belongs among the selected X points as a neighborpoint set of the point Px. In an embodiment, the neighbor point searchrange is the number of points. The neighbor point search range accordingto embodiments may include one or more points located before (i.e., infront of) and/or after (i.e., behind) the center point Pi in Mortonorder. X is the maximum number of points that may be registered asneighbor points.

According to embodiments, information about the neighbor point searchrange and information about the maximum number X of points that may beregistered as neighbor points may be configured by the neighbor pointset configuration unit 53008 or may be input as a user parameter orsignaled in signaling information through the signaling processor 51005(e.g., the lifting_search_range field andlifting_num_pred_nearest_neighbours field signaled in the APS).According to embodiments, an actual search range may be a value obtainedby multiplying the value of the lifting_search_range field by 2 and thenadding the center point to the resultant value (i.e., (value of thelifting_search_range field*2)+center point), a value obtained by addingthe center value to the value of the lifting_search_range field (i.e.,value of lifting_search_range field+center point), or the value of thelifting_search_range field. The present disclosure provides anembodiment of searching for neighbor points of the point Px in theretained list when the number of LODs is plural and searching forneighbor points of the point Px in the index list when the number ofLODs is one.

For example, if the number of LODs is 2 or more and the informationabout the search range (e.g., the lifting_search_range field) is 128,the actual search range includes 128 points before the center point Pi,128 points after the center point Pi, and the center point Pi in theretained list arranged with Morton codes. As another example, if thenumber of LODs is 1 and the information about the search range is 128,the actual search range includes 128 points before the center point Piand the center point Pi in the index list arranged with Morton codes. Asanother example, if the number of LODs is 1 and the information aboutthe search range is 128, the actual search range includes 128 pointsbefore the current point Px in the index list arranged with Mortoncodes.

According to embodiments, the neighbor point set configuration unit53008 compares the distance values between points in the actual searchrange and the point Px to search for X NN points and register onlypoints within the maximum neighbor point distance at an LOD to which thepoint Px belongs among the X points as the neighbor point set of thepoint Px. That is, the neighbor points registered as the neighbor pointset of the point Px are limited to points within the maximum neighborpoint distance at the LOD to which the point Px belongs among the Xpoints.

As described above, the present disclosure may provide an effect ofenhancing compression efficiency of attributes by limiting points thatmay be selected as the neighbor point set in consideration of attributecharacteristics (correlation) between points of point cloud content.

FIG. 27 is a diagram illustrating another example of searching forneighbor points by applying a search range and a maximum neighbor pointdistance according to embodiments. Arrows illustrated in the drawingindicate a Morton order according to embodiments.

Since the example of FIG. 27 is similar to that of FIG. 26 except for aposition to which the maximum neighbor point distance is applied, arepetitive description thereof will be omitted herein. Therefore, forportions omitted or not described in FIG. 27 , refer to the descriptionof FIG. 26 .

According to embodiments, points of the index list and points of theretained list are arranged in ascending order based on the size of aMorton code.

The neighbor point set configuration unit 53008 according to embodimentsmay search for a center point Pi having a Morton code nearest to aMorton code of a point Px among points located before the point Px inorder (i.e., points having Morton codes smaller than or equal to theMorton code of the point Px) among points belonging to LOD₀ to LOD_(l-1)sets and/or points belonging to the LOD_(l) set, in order to register aneighbor point set of the point Px (i.e., a point to be encoded or acurrent point) belonging to the LOD_(l) set.

The neighbor point set configuration unit 53008 according to embodimentscompares distance values between the point Px and points belonging to aneighbor point search range located before the searched (or selected)center point Pi (i.e., on the left side of the center point in FIG. 27 )and after the searched (or selected) center point Pi (i.e., on the rightside of the center point in FIG. 27 ). The neighbor point setconfiguration unit 53008 may select X (e.g., 3) NN points within themaximum neighbor point distance at an LOD to which the point Px belongsand register the X points as the neighbor point set of the point Px.

The present disclosure provides an embodiment of searching for neighborpoints of the point Px in an actual search range of the retained listwhen the number of LODs is plural and searching for the neighbor pointsof the point Px in the actual search range of the index list when thenumber of LODs is one.

For example, if the number of LODs is 2 or more and the informationabout the search range (e.g., the lifting_search_range field) is 128,the actual search range includes 128 points before the center point Pi,128 points after the center point Pi, and the center point Pi in theretained list arranged with Morton codes. As another example, if thenumber of LODs is 1 and the information about the search range is 128,the actual search range includes 128 points before the center point Piand the center point Pi in the index list arranged with Morton codes. Asanother example, if the number of LODs is 1 and the information aboutthe search range is 128, the actual search range includes 128 pointsbefore the current point Px in the index list arranged with Mortoncodes.

Upon comparing the distance values between points in the actual searchrange and the point Px, the neighbor point set configuration unit 53008according to embodiments selects X (e.g., 3) NN points within themaximum neighbor point distance at an LOD to which the point Px belongsfrom among points in the actual search range and registers the X pointsas the neighbor point set of the point Px. That is, the neighbor pointsregistered as the neighbor point set of the point Px are limited topoints within the maximum neighbor point distance at the LOD to whichthe point Px belongs among the X points.

As described above, the present disclosure may provide an effect ofenhancing compression efficiency of attributes by selecting the neighborpoint set in consideration of attribute characteristics (correlation)between points of point cloud content.

According to the embodiments described above, when the neighbor pointset is registered in each predictor of points to be encoded in theneighbor point set configuration unit 53008, the attribute informationprediction unit 53009 predicts an attribute value of a correspondingpoint from one or more neighbor points registered in the predictor. Asdescribed above, when configuring the neighbor point set, the number ofneighbor points included in the neighbor point set registered in eachpredictor is equal to or less than X (e.g., 3) by applying the maximumneighbor point distance. According to embodiments, a predictor of thepoint may register a ½ distance (=weight) based on a distance value toeach neighbor point by a registered neighbor point set. For example, thepredictor of the P3 node calculates a weight based on the distance valueto each neighbor point by (P2 P4 P6) as the neighbor point set.According to embodiments, weights of neighbor points may be(1/√(P2−P3)², 1/√(P4−P3)², 1/√(P6−P3)²).

According to embodiments, when the neighbor point set is registered inthe predictor, the neighbor point set configuration unit 53008 or theattribute information predicting unit 53009 may normalize a weight ofeach neighbor point by a total weight of neighbor points included in theneighbor point set.

For example, the weights of all neighbor points in the neighbor set ofnode P3 are added (total weight=1/√(P2−P3)²+1/√(P4−P3)²+1/√(P6−P3)²),and the weight of each neighbor point is divided by the sum of theweights (1/√(P2−P3)²/total_weight, 1/√(P4−P3)²/total_weight,1/√(P6−P3)²/total_weight). Thereby, the weight of each neighbor point isnormalized

Then, the attribute information prediction unit 5301 may predict anattribute value through the predictor.

According to embodiments, the average of the values obtained bymultiplying the attributes (e.g., color, reflectance, etc.) of neighborpoints registered in the predictor by a weight (or a normalized weight)may be set as a predicted result (i.e., a predicted attribute value).Alternatively, an attribute of a specific point may be set as apredicted result (i.e., a predicted attribute value). According toembodiments, the predicted attribute value may be referred to aspredicted attribute information. In addition, a residual attribute value(or residual attribute information or residual) may be obtainedsubtracting the predicted attribute value (or predicted attributeinformation) of the point from the attribute value (i.e., the originalattribute value) of the point.

According to embodiments, compressed result values may be pre-calculatedby applying various prediction modes (or predictor indexes), and then aprediction mode (i.e., a predictor index) generating the smallestbitstream may be selected from among the prediction modes.

Next, a process of selecting a prediction mode will be described indetail.

In this specification, a prediction mode has the same meaning as apredictor index (Preindex), and may be broadly referred to as aprediction method.

In an embodiment, the process of finding the most suitable predictionmode for each point and setting the found prediction mode in thepredictor of the corresponding point may be performed by the attributeinformation prediction unit 53009.

According to embodiments, a prediction mode in which a predictedattribute value is calculated through a weighted average (i.e., anaverage obtained by multiplying the attributes of neighbor points set inthe predictor of each point by a weight calculated based on the distanceto each neighbor point) will be referred to as prediction mode 0. Inaddition, a prediction mode in which the attribute of the first neighborpoint is set as a predicted attribute value will be referred to asprediction mode 1, a prediction mode in which the attribute of thesecond neighbor point is set as a predicted attribute value will bereferred to as prediction mode 2, and a prediction mode in which theattribute of the third neighbor point is set as a predicted attributevalue will be referred to as prediction mode 3. In other words, thevalue of the prediction mode (or predictor index) equal to 0 mayindicate that the attribute value is predicted through the weightedaverage, and the value equal to 1 may indicate that the attribute valueis predicted through the first neighbor node (i.e., the neighbor point).The value equal to 2 may indicate that the attribute value is predictedthrough the second neighbor node, and the value equal to 3 may indicatethat the attribute value is predicted through the third neighboringnode.

According to embodiments, a residual attribute value in prediction mode0, a residual attribute value in prediction mode 1, a residual attributevalue in prediction mode 2, and a residual attribute value in predictionmode 3 may be calculated, and a score or double score may be calculatedbased on each residual attribute value. Then, a prediction mode havingthe least calculated score may be selected and set as a prediction modeof the corresponding point.

According to embodiments, the process of finding the most suitableprediction mode among a plurality of prediction modes and setting thesame as a prediction mode of a corresponding point may be performed whena preset condition is satisfied. Accordingly, when the preset conditionis not satisfied, a pre-defined prediction mode, e.g., prediction mode0, in which a prediction attribute value is calculated through aweighted average, may be set as the prediction mode of the point withoutperforming the process of finding the most suitable prediction mode. Inan embodiment, this process is performed for each point.

According to embodiments, the preset condition may be satisfied for aspecific point when the difference in attribute elements (e.g., R, G, B)between neighbor points registered in the predictor of a point isgreater than or equal to a preset threshold (e.g.,lifting_adaptive_prediction_threshold), or when the differences inattribute elements (e.g., R, G, B) between the neighbor pointsregistered in the predictor of the point are calculated, and the sum ofthe largest differences for the elements is greater than or equal to apreset threshold. For example, suppose that point P3 is the specificpoint, and points P2, P4, and P6 are registered as neighbor points ofpoint P3. Additionally, suppose that, when differences in R, G, and Bvalues between points P2 and P4, differences in R, G, and B valuesbetween points P2 and P6, and differences in R, G, and B values betweenpoints P4 and P6 are calculated, the largest difference in R value isobtained between points P2 and P4, the largest difference in G value isobtained between points P4 and P6, and the largest difference in B valueis obtained between points P2 and P6. In addition, suppose that thedifference in R value between points P2 and P4 is the largest among thelargest difference in R value (between P2 and P4) and the largestdifference in G value (between P4 and P6), and the largest difference inB value (between P2 and P6).

On these assumptions, when the difference in R value between points P2and P4 is greater than or equal to a preset threshold, or when the sumof the difference in R value between points P2 and P4, the difference inG value between points P4 and P6, and the difference in B value betweenpoints P2 and P6 is greater than or equal to a preset threshold, aprocess of finding the most suitable prediction mode among a pluralityof candidate prediction modes may be performed. In addition, theprediction mode (e.g., predIndex) may be signaled only when thedifference in R value between points P2 and P4 is greater than or equalto a preset threshold, or the sum of the difference in R value betweenpoints P2 and P4, the difference in G value between points P4 and P6,and the difference in B value between points P2 and P6 is greater thanor equal to a preset threshold.

According to another embodiment, the preset condition may be satisfiedfor a specific point when the maximum difference between values of anattribute (e.g., reflectance) of neighbor points registered in thepredictor of the point is greater than or equal to a preset threshold(e.g., lifting_adaptive_prediction_threshold). For example, suppose thatthe difference in reflectance between points P2 and P4 is the largestamong the difference in reflectance between points P2 and P4, thedifference in reflectance between points P2 and P6, and the differencein reflectance between points P4 and P6.

On this assumption, the process of finding the most suitable predictionmode among a plurality of candidate prediction modes may be performedwhen the difference in reflectance between points P2 and P4 is greaterthan or equal to a preset threshold. In addition, the prediction mode(e.g., predIndex) may be signaled only when the difference inreflectance between points P2 and P4 is greater than or equal to thepreset threshold.

According to embodiments, the selected prediction mode (e.g., predIndex)of the corresponding point may be signaled in attribute slice data. Inthis case, the transmitting side transmits a residual attribute valueacquired based on the selected prediction mode and the receiving sideacquires a predicted attribute value of a corresponding point based onthe signaled prediction mode and adds the predicted attribute value andthe received residual attribute value to restore an attribute value ofthe corresponding point.

In another embodiment, when the prediction mode is not signaled, thetransmitting side may calculate a predicted attribute value based on aprediction mode (e.g., prediction mode 0) set as a default mode, andcalculate and transmit a residual attribute value based on thedifference between the original attribute value and the predictedattribute value. The receiving side may calculate a predicted attributevalue based on a prediction mode (e.g., prediction mode 0) set as adefault mode, and restore the attribute value by adding the value to thereceived residual attribute value.

According to embodiments, the threshold may be directly input orsignaled in the signaling information through the signaling processor51005 (e.g., the lifting_adaptive_prediction_threshold field signaled inthe APS).

According to embodiments, when a preset condition is satisfied for aspecific point as described above, predictor candidates may begenerated. The predictor candidates are referred to as prediction modesor predictor indexes.

According to embodiments, prediction modes 1 to 3 may be included in thepredictor candidates. According to embodiments, prediction mode 0 may ormay not be included in the predictor candidates. According toembodiments, at least one prediction mode not mentioned above may befurther included in the predictor candidates.

The prediction mode set for each point through the above-describedprocess and the residual attribute value in the set prediction mode areoutput to the residual attribute information quantization processor53010.

According to embodiments, the residual attribute informationquantization processor 53010 may apply zero run-length coding to theinput residual attribute values.

According to an embodiment of the present disclosure, quantization andzero run-length coding may be performed on the residual attributevalues.

According to embodiments, the arithmetic coder 53011 applies arithmeticcoding to the prediction modes and residual attribute values output fromthe residual attribute information quantization processor 53010 andoutputs the result as an attribute bitstream.

The geometry bitstream compressed and output by the geometry encoder51006 and the attribute bitstream compressed and output by the attributeencoder 51007 are output to the transmission processor 51008.

The transmission processor 51008 according to the embodiments mayperform an operation and/or transmission method identical or similar tothe operation and/or transmission method of the transmission processor12012 of FIG. 12 , and perform an operation and/or transmission methodidentical or similar to the operation and/or transmission method of thetransmitter 10003 of FIG. 1 . For details, reference will be made to thedescription of FIG. 1 or 12 .

The transmission processor 51008 according to the embodiments maytransmit the geometry bitstream output from the geometry encoder 51006,the attribute bitstream output from the attribute encoder 51007, and thesignaling bitstream output from the signaling processor 51005,respectively, or may multiplex the bitstreams into one bitstream to betransmitted.

The transmission processor 51008 according to the embodiments mayencapsulate the bitstream into a file or segment (e.g., a streamingsegment) and then transmit the encapsulated bitstream over variousnetworks such as a broadcasting network and/or a broadband network.

The signaling processor 51005 according to the embodiments may generateand/or process signaling information and output the same to thetransmission processor 51008 in the form of a bitstream. The signalinginformation generated and/or processed by the signaling processor 51005will be provided to the geometry encoder 51006, the attribute encoder51007, and/or the transmission processor 51008 for geometry encoding,attribute encoding, and transmission processing. Alternatively, thesignaling processor 51005 may receive signaling information generated bythe geometry encoder 51006, the attribute encoder 51007, and/or thetransmission processor 51008.

In the present disclosure, the signaling information may be signaled andtransmitted on a per parameter set (sequence parameter set (SPS),geometry parameter set (GPS), attribute parameter set (APS), tileparameter set (TPS), or the like) basis. Also, it may be signaled andtransmitted on the basis of a coding unit of each image, such as sliceor tile. In the present disclosure, signaling information may includemetadata (e.g., set values) related to point cloud data, and may beprovided to the geometry encoder 51006, the attribute encoder 51007,and/or the transmission processor 51008 for geometry encoding, attributeencoding, and transmission processing. Depending on the application, thesignaling information may also be defined at the system side, such as afile format, dynamic adaptive streaming over HTTP (DASH), or MPEG mediatransport (MMT), or at the wired interface side, such as high definitionmultimedia interface (HDMI), Display Port, Video Electronics StandardsAssociation (VESA), or CTA.

A method/device according to the embodiments may signal relatedinformation to add/perform an operation of the embodiments. Thesignaling information according to the embodiments may be used in atransmission device and/or a reception device.

According to an embodiment of the present disclosure, information on themaximum number of predictors to be used for attribute prediction(lifting_max_num_direct_predictors), threshold information for enablingadaptive prediction of an attribute(lifting_adaptive_prediction_threshold), neighbor pointselection-related option information, and the like may be signaled in atleast one of a sequence parameter set, an attribute parameter set, atile parameter set, or an attribute slice header. In addition, accordingto an embodiment, predictor index information (predIndex) indicating aprediction mode corresponding to a predictor candidate selected fromamong a plurality of predictor candidates may be signaled in attributeslice data.

According to embodiments, the neighbor point selection-related optioninformation may include information related to NN_range (e.g.,nearest_neighbor_max_range). According to embodiments, the neighborpoint selection-related option information may further include at leastone of information about a maximum number that may be set as a neighborpoint (e.g., lifting_num_pred_nearest_neighbours), information relatedto a search range (e.g., lifting_search_range), information about a LODconfiguration method and/or information related to a base neighbor pointdistance.

Similar to the point cloud video encoder of the transmission devicedescribed above, the point cloud video decoder of the reception deviceperforms the same or similar process of generating an LOD_(l) set,finding the nearest neighbor points based on the LOD_(l) set andregistering the same in the predictor as a neighbor point set,calculating a weight based on a distance from each neighbor point, andperforming normalization with the weight. Then, the decoder decodes areceived prediction mode, and predicts an attribute value of the pointaccording to the decoded prediction mode. In addition, after thereceived residual attribute value is decoded, the decoded residualattribute value may be added to the predicted attribute value to restorethe attribute value of the point.

FIG. 28 is a diagram showing another exemplary point cloud receptiondevice according to embodiments.

The point cloud reception device according to the embodiments mayinclude a reception processor 61001, a signaling processor 61002, ageometry decoder 61003, an attribute decoder 61004, and a post-processor61005. According to embodiments, the geometry decoder 61003 and theattribute decoder 61004 may be referred to as a point cloud videodecoder. According to embodiments, the point cloud video decoder may bereferred to as a PCC decoder, a PCC decoding unit, a point clouddecoder, a point cloud decoding unit, or the like.

The reception processor 61001 according to the embodiments may receive asingle bitstream, or may receive a geometry bitstream, an attributebitstream, and a signaling bitstream, respectively. When a file and/orsegment is received, the reception processor 61001 according to theembodiments may decapsulate the received file and/or segment and outputthe decapsulated file and/or segment as a bitstream.

When the single bitstream is received (or decapsulated), the receptionprocessor 61001 according to the embodiments may demultiplex thegeometry bitstream, the attribute bitstream, and/or the signalingbitstream from the single bitstream. The reception processor 61001 mayoutput the demultiplexed signaling bitstream to the signaling processor61002, the geometry bitstream to the geometry decoder 61003, and theattribute bitstream to the attribute decoder 61004.

When the geometry bitstream, the attribute bitstream, and/or thesignaling bitstream are received (or decapsulated), respectively, thereception processor 61001 according to the embodiments may deliver thesignaling bitstream to the signaling processor 61002, the geometrybitstream to the geometry decoder 61003, and the attribute bitstream tothe attribute decoder 61004.

The signaling processor 61002 may parse signaling information, forexample, information contained in the SPS, GPS, APS, TPS, metadata, orthe like from the input signaling bitstream, process the parsedinformation, and provide the processed information to the geometrydecoder 61003, the attribute decoder 61004, and the post-processor61005. In another embodiment, signaling information contained in thegeometry slice header and/or the attribute slice header may also beparsed by the signaling processor 61002 before decoding of thecorresponding slice data. That is, when the point cloud data ispartitioned into tiles and/or slices at the transmitting side as shownin FIGS. 16A to 16C, the TPS includes the number of slices included ineach tile, and accordingly the point cloud video decoder according tothe embodiments may check the number of slices and quickly parse theinformation for parallel decoding.

Accordingly, the point cloud video decoder according to the presentdisclosure may quickly parse a bitstream containing point cloud data asit receives an SPS having a reduced amount of data. The reception devicemay decode tiles upon receiving the tiles, and may decode each slicebased on the GPS and APS included in each tile. Thereby, decodingefficiency may be maximized.

That is, the geometry decoder 61003 may reconstruct the geometry byperforming the reverse process of the operation of the geometry encoder51006 of FIG. 15 on the compressed geometry bitstream based on signalinginformation (e.g., geometry related parameters). The geometry restored(or reconstructed) by the geometry decoder 61003 is provided to theattribute decoder 61004. The attribute decoder 61004 may restore theattribute by performing the reverse process of the operation of theattribute encoder 51007 of FIG. 15 on the compressed attribute bitstreambased on signaling information (e.g., attribute related parameters) andthe reconstructed geometry. According to embodiments, when the pointcloud data is partitioned into tiles and/or slices at the transmittingside as shown in FIGS. 16A to 16C, the geometry decoder 61003 and theattribute decoder 61004 perform geometry decoding and attribute decodingon a tile-by-tile basis and/or slice-by-slice basis.

FIG. 29 is a detailed block diagram illustrating another example of thegeometry decoder 61003 and the attribute decoder 61004 according toembodiments.

The arithmetic decoder 63001, the octree reconstruction unit 63002, thegeometry information prediction unit 63003, the inverse quantizationprocessor 63004, and the coordinates inverse transformation unit 63005included in the geometry decoder 61003 of FIG. 29 may perform some orall of the operations of the arithmetic decoder 11000, the octreesynthesis unit 11001, the surface approximation synthesis unit 11002,the geometry reconstruction unit 11003, and the coordinates inversetransformation unit 11004 of FIG. 11 , or may perform some or all of theoperations of the arithmetic decoder 13002, the occupancy code-basedoctree reconstruction processor 13003, the surface model processor13004, and the inverse quantization processor 13005 of FIG. 13 . Thepositions restored by the geometry decoder 61003 are output to thepost-processor 61005.

According to embodiments, when information on the maximum number ofpredictors to be used for attribute prediction(lifting_max_num_direct_predictors), threshold information for enablingadaptive prediction of an attribute(lifting_adaptive_prediction_threshold), neighbor pointselection-related information, and the like are signaled in at least oneof a sequence parameter set (SPS), an attribute parameter set (APS), atile parameter set (TPS), or an attribute slice header, they may beacquired and provided to the attribute decoder 61004 by the signalingprocessor 61002, or may be acquired directly by the attribute decoder61004.

The attribute decoder 61004 according to the embodiments may include anarithmetic decoder 63006, an LOD configuration unit 63007, a neighborpoint configuration unit 63008, an attribute information prediction unit63009, and a residual attribute information inverse quantizationprocessor 63010, and an inverse color transformation processor 63011.

The arithmetic decoder 63006 according to the embodiments mayarithmetically decode the input attribute bitstream. The arithmeticdecoder 63006 may decode the attribute bitstream based on thereconstructed geometry. The arithmetic decoder 63006 performs anoperation and/or decoding identical or similar to the operation and/ordecoding of the arithmetic decoder 11005 of FIG. 11 or the arithmeticdecoder 13007 of FIG. 13 .

According to embodiments, the attribute bitstream output from thearithmetic decoder 63006 may be decoded by one or a combination of twoor more of RAHT decoding, LOD-based predictive transform decoding, andlifting transform decoding based on the reconstructed geometryinformation.

It has been described as an embodiment that the transmission deviceperforms attribute compression by one or a combination of the LOD-basedpredictive transform coding and the lifting transform coding.Accordingly, an embodiment in which the reception device performsattribute decoding by one or a combination of the LOD-based predictivetransform decoding and the lifting transform decoding will be described.The description of the RAHT decoding for the reception device will beomitted.

According to an embodiment, an attribute bitstream that isarithmetically decoded by the arithmetic decoder 63006 is provided tothe LOD configuration unit 63007. According to embodiments, theattribute bitstream provided from the arithmetic decoder 63006 to theLOD configuration unit 63007 may contain prediction modes and residualattribute values.

The LOD configuration unit 63007 according to the embodiments generatesone or more LODs in the same or similar manner as the LOD configurationunit 53007 of the transmission device, and outputs the generated one ormore LODs to the neighbor point configuration unit 63008.

According to embodiments, the LOD configuration unit 63007 may configureone or more LODs using one or more LOD generation methods (or LODconfiguration methods). According to embodiments, the LOD generationmethod used in the LOD configuration unit 63007 may be provided throughthe signaling processor 61002. For example, the LOD generation methodmay be signaled in an APS of signaling information. According toembodiments, the LOD generation method may be classified into anoctree-based LOD generation method, a distance-based LOD generationmethod, and a sampling-based LOD generation method.

According to embodiments, a group having different LODs is referred toas an LOD_(l) set. Here, l represents an LOD as an integer starting from0. LOD₀ is a set consisting of points having the largest distancetherebetween. As l increases, the distance between points belonging toLOD_(l) decreases.

According to embodiments, prediction modes and residual attributevalues, encoded by the transmission device, may be provided for eachLODs or only for a leaf node.

In one embodiment, when the LOD_(l) set is generated by the LODconfiguration unit 63007, the neighbor point set configuration unit63008 may search for neighbor points equal to or less than X (e.g., 3)points in a group having the same or lower LOD (i.e., a large distancebetween nodes) based on the LOD_(l) set and register the searchedneighbor points as a neighbor point set in the predictor.

According to an embodiment, the neighbor point set configuration unit63008 configures the neighbor point set by applying a search rangeand/or a maximum neighbor point distance based on the signalinginformation.

The neighbor point set configuration unit 63008 according to embodimentsmay obtain the maximum neighbor point distance by multiplying a baseneighbor point distance by NN_range. NN_range is a range within which aneighbor point may be selected and will be referred to as a maximumneighbor point range, a neighbor point range, or an NN_range.

The neighbor point set configuration unit 63008 according to embodimentsmay set the NN_range automatically or manually according tocharacteristics of content or may be received through the signalingprocessor 61002. For example, information about NN_range may be signaledin at least one of an SPS, an APS, a tile parameter set, or an attributeslice header of the signaling information.

The neighbor point set configuration unit 63008 according to embodimentsmay calculate/configure the base neighbor point distance by combiningone or more of an octree-based method, a distance-based method, asampling-based method, a Morton code average difference-based method foreach LOD, and an average distance difference-based method for each LOD.According to embodiments, the neighbor point set configuration unit63008 may receive information about the base neighbor point distancethrough the signaling processor 61002, and the information about thebase neighbor point distance may be signaled in the APS of the signalinginformation.

Since a detailed description of calculating/configuring the baseneighbor point distance by combining one or more of the octree-basedmethod, the distance-based method, the sampling-based method, the Mortoncode average difference-based method for each LOD, and the averagedistance difference-based method for each LOD in the neighbor point setconfiguration unit 63008 according to embodiments has been given indetail in the above-described process of encoding an attribute of thetransmission device, details thereof will be omitted herein.

According to an embodiment, if the maximum neighbor point distance isdetermined as described above, the neighbor point set configuration unit63008 searches for X (e.g., 3) NN points among points within the searchrange in a group having the same or lower LOD (i.e., a large distancebetween nodes) based on the LOD_(l) set as in FIG. 26 . Then theneighbor point set configuration unit 63008 may register only NN pointswithin the maximum neighbor point distance among the X (e.g., 3) NNpoints as a neighbor point set. Therefore, the number of NN pointsregistered as the neighbor point set is equal to or smaller than X. Inother words, NN points that are not within the maximum neighbor pointdistance among the X NN points are not registered as the neighbor pointset and are excluded from the neighbor point set. For example, if two ofthe three NN points are not within the maximum neighbor point distance,the two NN points are excluded and only the other NN point, i.e., one NNpoint, is registered as the neighbor point set.

According to another embodiment, if the maximum neighbor point distanceis determined as described above, the neighbor point set configurationunit 53008 may search for X (e.g., 3) NN points within the maximumneighbor point distance among points within the search range in a grouphaving the same or lower LOD (i.e., a large distance between nodes)based on the LOD₁ set as in FIG. 27 and register the X NN points as theneighbor point set.

That is, the number of NN points that may be registered as the neighborpoint set may vary according to a timing (or position) of applying themaximum neighbor point distance. In other words, the number of NN pointsregistered as the neighbor point set may differ according to whether themaximum neighbor point distance is applied after searching for X NNpoints through neighbor point search or the maximum neighbor pointdistance is applied when calculating a distance between points as inFIG. 27 .

Referring to FIG. 26 as an example, the neighbor point set configurationunit 63008 compares the distance values between points within the actualsearch range and the point Px to search for X NN points and registersonly points within the maximum neighbor point distance at an LOD towhich the point Px belongs among the X points as the neighbor point setof the point Px. That is, the neighbor points registered as the neighborpoint set of the point Px are limited to points within the maximumneighbor point distance at the LOD to which the point Px belongs amongthe X points.

Referring to FIG. 27 as an example, when comparing the distance valuesbetween the points within the actual search range and the point Px, theneighbor point set configuration unit 63008 selects X (e.g., 3) NNpoints within the maximum neighbor point distance at an LOD to which thepoint Px belongs from among points in the actual search range andregisters the selected points as the neighbor point set of the point Px.That is, neighbor points registered as the neighbor point set of thepoint Px are limited to points within the maximum neighbor pointdistance at the LOD to which the point Px belongs.

In FIGS. 26 and 27 , the actual search range may be a value obtained bymultiplying the value of the lifting_search_range field by 2 and thenadding a center point to the resultant value (i.e., (value of thelifting_search_range field*2)+center point), a value obtained by addingthe center value to the value of the lifting_search_range field (i.e.,value of lifting_search_range field+center point), or the value of thelifting_search_range field. In an embodiment, the neighbor point setconfiguration unit 63008 searches for neighbor points of the point Px inthe retained list when the number of LODs is plural and searches forneighbor points of the point Px in the index list when the number ofLODs is one.

For example, if the number of LODs is 2 or more and information aboutthe search range (e.g., lifting_search_range field) is 128, the actualsearch range includes 128 points before the center point Pi, 128 pointsafter the center point Pi, and the center point Pi in the retained listarranged with Morton codes. As another example, if the number of LODs is1 and the information about the search range is 128, the actual searchrange includes 128 points before the center point Pi and the centerpoint Pi in the index list arranged with Morton codes. As anotherexample, if the number of LODs is 1 and the information about the searchrange is 128, the actual search range includes 128 points before thecurrent point Px in the index list arranged with Morton codes.

As an example, it is assumed that the neighbor point set configurationunit 63008 selects P2 P4 P6 points as neighbor points of a P3 point(i.e., node) belonging to LOD_(l) and registers the selected points asthe neighbor point set in the predictor of P3 (see FIG. 9 ).

According to embodiments, when the neighbor point set is registered ineach predictor of points to be decoded in the neighbor point setconfiguration unit 63008, the attribute information predicting unit63009 predicts an attribute value of a corresponding point from one ormore neighbor points registered in each predictor. According toembodiments, the attribute information predictor 63009 performs aprocess of predicting an attribute value of the point based on aprediction mode of the point. This attribute prediction process isperformed on all points or at least some points of reconstructedgeometry.

The prediction mode of a specific point according to embodiments may beone of prediction mode 0 to prediction mode 3.

According to embodiments, prediction mode 0 is a mode that calculates aprediction attribute value through a weighted average, prediction mode 1is a mode that determines an attribute of a first neighbor point as theprediction attribute value, prediction mode 2 is a mode that determinesan attribute of a second neighbor point as the prediction attributevalue, and prediction mode 3 is a mode that determines an attribute of athird neighbor point as the prediction attribute value.

According to embodiments, when the maximum difference between attributevalues of the neighbor points registered in the predictor of the pointis less than a preset threshold, the attribute encoder of thetransmitting side sets prediction mode 0 as the prediction mode of thepoint. When the maximum difference is greater than or equal to thepreset threshold, the attribute encoder applies the RDO method to aplurality of candidate prediction modes, and sets one of the candidateprediction modes as a prediction mode of the point. In an embodiment,this process is performed for each point.

According to embodiments, a prediction mode (predIndex) of the pointselected by applying the RDO method may be signaled in attribute slicedata. Accordingly, the prediction mode of the point may be obtained fromthe attribute slice data.

According to embodiments, the attribute information prediction unit63009 may predict the attribute value of each point based on theprediction mode of each point that is set as described above.

For example, when it is assumed that the prediction mode of point P3 isprediction mode 0, the average of the values obtained by multiplying theattributes of points P2, P4, and P6, which are neighbor pointsregistered in the predictor of point P3, by a weight (or normalizedweight) may be calculated. The calculated average may be determined asthe predicted attribute value of the point.

As another example, when it is assumed that the prediction mode of pointP3 is prediction mode 1, the attribute value of point P4, which is aneighbor point registered in the predictor of point P3, may bedetermined as the predicted attribute value of point P3.

As another example, when it is assumed that the prediction mode of pointP3 is prediction mode 2, the attribute value of point P6, which is aneighbor point registered in the predictor of point P3, may bedetermined as the predicted attribute value of point P3.

As another example, when it is assumed that the prediction mode of pointP3 is prediction mode 3, the attribute value of point P2, which is aneighbor point registered in the predictor of point P3, may bedetermined as the predicted attribute value of point P3.

Once the attribute information prediction unit 63009 obtains thepredicted attribute value of the point based on the prediction mode ofthe point, the residual attribute information inverse quantizationprocessor 63010 restores the attribute value of the point by adding thepredicted attribute value of the point predicted by the predictor 63009to the received residual attribute value of the point, and then performsinverse quantization as a reverse process to the quantization process ofthe transmission device.

In an embodiment, in the case where the transmitting side applies zerorun-length coding to the residual attribute values of points, theresidual attribute information inverse quantization processor 63010performs zero run-length decoding on the residual attribute values ofthe points, and then performs inverse quantization.

The attribute values restored by the residual attribute informationinverse quantization processor 63010 are output to the inverse colortransformation processor 63011.

The inverse color transformation processor 63011 performs inversetransform coding for inverse transformation of the color values (ortextures) included in the restored attribute values, and then outputsthe attributes to the post-processor 61005. The inverse colortransformation processor 63011 performs an operation and/or inversetransform coding identical or similar to the operation and/or inversetransform coding of the inverse color transformation unit 11010 of FIG.11 or the inverse color transformation processor 13010 of FIG. 13 .

The post-processor 61005 may reconstruct point cloud data by matchingthe positions restored and output by the geometry decoder 61003 with theattributes restored and output by the attribute decoder 61004. Inaddition, when the reconstructed point cloud data is in a tile and/orslice unit, the post-processor 61005 may perform a reverse process tothe spatial partitioning of the transmitting side based on the signalinginformation. For example, when the bounding box shown in FIG. 16A ispartitioned into tiles and slices as shown in of FIG. 16B and FIG. 16C,the tiles and/or slices may be combined based on the signalinginformation to restore the bounding box as shown in FIG. 16A.

FIG. 30 illustrates an example of a bitstream structure of point clouddata for transmission/reception according to embodiments.

Related information may be signaled to add/perform the embodimentsdescribed above. The signaling information according to embodiments maybe used in the point cloud video encoder of a transmission end or in thepoint cloud video decoder of a reception end.

The point cloud video encoder according to embodiments may generate abitstream as illustrated in FIG. 32 by encoding the geometry informationand the attribute information as described above. In addition, signalinginformation about the point cloud data may be generated and processed byat least one of the geometry encoder, the attribute encoder, or thesignaling processor of the point cloud video encoder and may be includedin a bitstream.

The signaling information according to embodiments may bereceived/obtained by at least one of the geometry decoder, the attributedecoder, and the signaling processor of the point cloud video decoder.

The bitstream according to embodiments may be divided into a geometrybitstream, an attribute bitstream, and a signaling bitstream to betransmitted/received or may be combined into one bitstream andtransmitted/received.

When a geometry bitstream, an attribute bitstream, and a signalingbitstream according to embodiments are configured as one bitstream, thebitstream may include one or more sub-bitstreams. The bitstreamaccording to the embodiments may include a sequence parameter set (SPS)for sequence level signaling, a geometry parameter set (GPS) forsignaling of geometry information coding, one or more attributeparameter sets (APSs) (APS₀, APS₁) for signaling of attributeinformation coding, a tile parameter set (TPS) for tile level signaling,and one or more slices (slice 0 to slice n). That is, a bitstream ofpoint cloud data according to embodiments may include one or more tiles,and each of the tiles may be a group of slices including one or moreslices (slice 0 to slice n). The TPS according to the embodiments maycontain information about each of the one or more tiles (e.g.,coordinate value information and height/size information about thebounding box). Each slice may include one geometry bitstream (Geom0) andone or more attribute bitstreams (Attr0 and Attr1). For example, a firstslice (slice 0) may include one geometry bitstream (Geom0⁰) and one ormore attribute bitstreams (Attr0⁰, Attr1⁰).

The geometry bitstream (or geometry slice) in each slice may be composedof a geometry slice header (geom_slice_header) and geometry slice data(geom_slice_data). According to embodiments, geom_slice_header mayinclude identification information (geom_parameter_set_id), a tileidentifier (geom_tile_id), and a slice identifier (geom_slice_id) for aparameter set included in the GPS, and information (geomBoxOrigin,geom_box_log2_scale, geom_max_node_size_log2, geom_num_points) aboutdata contained in the geometry slice data (geom_slice_data).geomBoxOrigin is geometry box origin information indicating the originof the box of the geometry slice data, geom_box_log2_scale isinformation indicating the log scale of the geometry slice data,geom_max_node_size_log2 is information indicating the size of the rootgeometry octree node, and geom_num_points is information related to thenumber of points of the geometry slice data. According to embodiments,the geom_slice_data may include geometry information (or geometry data)about the point cloud data in a corresponding slice.

Each attribute bitstream (or attribute slice) in each slice may becomposed of an attribute slice header (attr_slice_header) and attributeslice data (attr_slice_data). According to embodiments, theattr_slice_header may include information about the correspondingattribute slice data. The attribute slice data may contain attributeinformation (or attribute data or attribute value) about the point clouddata in the corresponding slice. When there is a plurality of attributebitstreams in one slice, each of the bitstreams may contain differentattribute information. For example, one attribute bitstream may containattribute information corresponding to color, and another attributestream may contain attribute information corresponding to reflectance.

FIG. 31 shows an exemplary bitstream structure for point cloud dataaccording to embodiments.

FIG. 32 illustrates a connection relationship between components in abitstream of point cloud data according to embodiments.

The bitstream structure for the point cloud data illustrated in FIGS. 31and 32 may represent the bitstream structure for point cloud data shownin FIG. 30 .

According to the embodiments, the SPS may include an identifier(seq_parameter_set_id) for identifying the SPS, and the GPS may includean identifier (geom_parameter_set_id) for identifying the GPS and anidentifier (seq_parameter_set_id) indicating an active SPS to which theGPS belongs. The APS may include an identifier (attr_parameter_set_id)for identifying the APS and an identifier (seq_parameter_set_id)indicating an active SPS to which the APS belongs.

According to embodiments, a geometry bitstream (or geometry slice) mayinclude a geometry slice header and geometry slice data. The geometryslice header may include an identifier (geom_parameter_set_id) of anactive GPS to be referred to by a corresponding geometry slice.Moreover, the geometry slice header may further include an identifier(geom_slice_id) for identifying a corresponding geometry slice and/or anidentifier (geom_tile_id) for identifying a corresponding tile. Thegeometry slice data may include geometry information belonging to acorresponding slice.

According to embodiments, an attribute bitstream (or attribute slice)may include an attribute slice header and attribute slice data. Theattribute slice header may include an identifier (attr_parameter_set_id)of an active APS to be referred to by a corresponding attribute sliceand an identifier (geom_slice_id) for identifying a geometry slicerelated to the attribute slice. The attribute slice data may includeattribute information belonging to a corresponding slice.

That is, the geometry slice refers to the GPS, and the GPS refers theSPS. In addition, the SPS lists available attributes, assigns anidentifier to each of the attributes, and identifies a decoding method.The attribute slice is mapped to output attributes according to theidentifier. The attribute slice has a dependency on the preceding(decoded) geometry slice and the APS. The APS refers to the SPS.

According to embodiments, parameters necessary for encoding of the pointcloud data may be newly defined in a parameter set of the point clouddata and/or a corresponding slice header. For example, when encoding ofthe attribute information is performed, the parameters may be added tothe APS. When tile-based encoding is performed, the parameters may beadded to the tile and/or slice header.

As shown in FIGS. 30, 31, and 32 , the bitstream of the point cloud dataprovides tiles or slices such that the point cloud data may bepartitioned and processed by regions. According to embodiments, therespective regions of the bitstream may have different importances.Accordingly, when the point cloud data is partitioned into tiles, adifferent filter (encoding method) and a different filter unit may beapplied to each tile. When the point cloud data is partitioned intoslices, a different filter and a different filter unit may be applied toeach slice.

When the point cloud data is partitioned and compressed, thetransmission device and the reception device according to theembodiments may transmit and receive a bitstream in a high-level syntaxstructure for selective transmission of attribute information in thepartitioned regions.

The transmission device according to the embodiments may transmit pointcloud data according to the bitstream structure as shown in FIGS. 30,31, and 32 . Accordingly, a method to apply different encodingoperations and use a good-quality encoding method for an importantregion may be provided. In addition, efficient encoding and transmissionmay be supported according to the characteristics of point cloud data,and attribute values may be provided according to user requirements.

The reception device according to the embodiments may receive the pointcloud data according to the bitstream structure as shown in FIGS. 30,31, and 32 . Accordingly, different filtering (decoding) methods may beapplied to the respective regions (regions partitioned into tiles orinto slices), rather than a complexly decoding (filtering) method beingapplied to the entire point cloud data. Therefore, better image qualityin a region important is provided to the user and an appropriate latencyto the system may be ensured.

As described above, a tile or a slice is provided to process the pointcloud data by partitioning the point cloud data by region. Inpartitioning the point cloud data by region, an option to generate adifferent set of neighbor points for each region may be set. Thereby, aselection method having low complexity and slightly lower reliability,or a selection method having high complexity and high reliability may beprovided.

Neighbor point selection-related option information for a sequencerequired in a process of encoding/decoding attribute informationaccording to embodiments may be signaled in an SPS and/or an APS.

According to embodiments, if a tile or slice having different attributecharacteristics is present in the same sequence, the neighbor pointselection-related option information for the sequence may be signaled ina TPS and/or an attribute slice header for each slice.

According to embodiments, when the point cloud data is divided byregions, attribute characteristics of a specific region may be differentfrom attribute characteristics of the sequence and thus may bedifferently configured through a different maximum neighbor point rangeconfiguration function.

Therefore, when the point cloud data is divided by tiles, a differentmaximum neighbor point range may be applied to each tile. In addition,when the point cloud data is divided by slices, a different maximumneighbor point range may be applied to each slice.

According to embodiments, at least one of the SPS, the APS, the TPS, orthe attribute slice header for each slice may include the neighbor pointselection-related option information. According to embodiments, theneighbor point selection-related option information may includeinformation about the maximum neighbor point range (e.g., anearest_neighbor_max_range field).

According to embodiments, the neighbor point selection-related optioninformation may further include at least one of information about themaximum number of points that may be set as neighbor points (e.g., thelifting_num_pred_nearest_neighbours field), information about a searchrange (e.g., the lifting_search_range field), and information about anLOD configuration method, or information about a base neighbor pointdistance.

A field, which is the term used in syntaxes of the present disclosuredescribed later, may have the same meaning as a parameter or an element.

FIG. 33 shows an embodiment of a syntax structure of a sequenceparameter set (SPS) (seq_parameter_set_rbsp( )) according to the presentdisclosure. The SPS may include sequence information about a point clouddata bitstream. In particular, in this example, the SPS includesneighbor point selection-related option information.

The SPS according to the embodiments may include a profile_idc field, aprofile_compatibility_flags field, a level_idc field, ansps_bounding_box_present_flag field, an sps_source_scale_factor field,an sps_seq_parameter_set_id field, an sps_num_attribute_sets field, andan sps_extension_present_flag field.

The profile_idc field indicates a profile to which the bitstreamconforms.

The profile_compatibility_flags field equal to 1 may indicate that thebitstream conforms to the profile indicated by profile_idc.

The level_idc field indicates a level to which the bitstream conforms.

The sps_bounding_box_present_flag field indicates whether sourcebounding box information is signaled in the SPS. The source bounding boxinformation may include offset and size information about the sourcebounding box. For example, the sps_bounding_box_present_flag field equalto 1 indicates that the source bounding box information is signaled inthe SPS. The sps_bounding_box_present_flag field equal to 0 indicatesthe source bounding box information is not signaled. Thesps_source_scale_factor field indicates the scale factor of the sourcepoint cloud.

The sps_seq_parameter_set_id field provides an identifier for the SPSfor reference by other syntax elements.

The sps_num_attribute_sets field indicates the number of codedattributes in the bitstream.

The sps_extension_present_flag field specifies whether thesps_extension_data syntax structure is present in the SPS syntaxstructure. For example, the sps_extension_present_flag field equal to 1specifies that the sps_extension_data syntax structure is present in theSPS syntax structure. The sps_extension_present_flag field equal to 0specifies that this syntax structure is not present. When not present,the value of the sps_extension_present_flag field is inferred to beequal to 0.

When the sps_bounding_box_present_flag field is equal to 1, the SPSaccording to embodiments may further include ansps_bounding_box_offset_x field, an sps_bounding_box_offset_y field, ansps_bounding_box_offset_z field, an sps_bounding_box_scale_factor field,an sps_bounding_box_size_width field, an sps_bounding_box_size_heightfield, and an sps_bounding_box_size_depth field.

The sps_bounding_box_offset_x field indicates the x offset of the sourcebounding box in the Cartesian coordinates. When the x offset of thesource bounding box is not present, the value ofsps_bounding_box_offset_x is 0.

The sps_bounding_box_offset_y field indicates the y offset of the sourcebounding box in the Cartesian coordinates. When the y offset of thesource bounding box is not present, the value ofsps_bounding_box_offset_y is 0.

The sps_bounding_box_offset_z field indicates the z offset of the sourcebounding box in the Cartesian coordinates. When the z offset of thesource bounding box is not present, the value ofsps_bounding_box_offset_z is 0.

The sps_bounding_box_scale_factor field indicates the scale factor ofthe source bounding box in the Cartesian coordinates. When the scalefactor of the source bounding box is not present, the value ofsps_bounding_box_scale_factor may be 1.

The sps_bounding_box_size_width field indicates the width of the sourcebounding box in the Cartesian coordinates. When the width of the sourcebounding box is not present, the value of thesps_bounding_box_size_width field may be 1.

The sps_bounding_box_size_height field indicates the height of thesource bounding box in the Cartesian coordinates. When the height of thesource bounding box is not present, the value of thesps_bounding_box_size_height field may be 1.

The sps_bounding_box_size_depth field indicates the depth of the sourcebounding box in the Cartesian coordinates. When the depth of the sourcebounding box is not present, the value of thesps_bounding_box_size_depth field may be 1.

The SPS according to embodiments includes an iteration statementrepeated as many times as the value of the sps_num_attribute_sets field.In an embodiment, i is initialized to 0, and is incremented by 1 eachtime the iteration statement is executed. The iteration statement isrepeated until the value of i becomes equal to the value of thesps_num_attribute_sets field. The iteration statement may include anattribute_dimension[i] field, an attribute_instance_id[i] field, anattribute_bitdepth[i] field, an attribute_cicp_colour_primaries[i]field, an attribute_cicp_transfer_characteristics[i] field, anattribute_cicp_matrix_coeffs[i] field, anattribute_cicp_video_full_range_flag[i] field, and a knownattribute_label_flag[i] field.

The attribute_dimension[i] field specifies the number of components ofthe i-th attribute.

The attribute_instance_id[i] field specifies the instance ID of the i-thattribute.

The attribute_bitdepth[i] field specifies the bitdepth of the i-thattribute signal(s).

The attribute_cicp_colour_primaries[i] field indicates chromaticitycoordinates of the color attribute source primaries of the i-thattribute.

The attribute_cicp_transfer_characteristics[i] field either indicatesthe reference opto-electronic transfer characteristic function of thecolour attribute as a function of a source input linear opticalintensity with a nominal real-valued range of 0 to 1 or indicates theinverse of the reference electro-optical transfer characteristicfunction as a function of an output linear optical intensity.

The attribute_cicp_matrix_coeffs[i] field describes the matrixcoefficients used in deriving luma and chroma signals from the green,blue, and red, or Y, Z, and X primaries.

The attribute_cicp_video_full_range_flag[i] field indicates the blacklevel and range of the luma and chroma signals as derived from E′Y,E′PB, and E′PR or E′R, E′G, and E′B real-valued component signals.

The known attribute_label_flag[i] field specifies whether aknown_attribute_label field or an attribute_label_four_bytes field issignaled for the i-th attribute. For example, the value of the knownattribute_label_flag[i] field equal to 0 specifies that theknown_attribute_label field is signaled for the i-th attribute. Theknown attribute_label_flag[i] field equal to 1 specifies that theattribute_label_four_bytes field is signaled for the i-th attribute.

The known_attribute_label[i] field may specify an attribute type. Forexample, the known_attribute_label[i] field equal to 0 may specify thatthe i-th attribute is color. The known_attribute_label[i] field equal to1 specifies that the i-th attribute is reflectance. Theknown_attribute_label[i] field equal to 2 may specify that the i-thattribute is frame index.

The attribute_label_four_bytes field indicates the known attribute typewith a 4-byte code.

In this example, the attribute_label_four_bytes field indicates colorwhen equal to 0 and indicates reflectance when is equal to 1.

According to embodiments, when the sps_extension_present_flag field isequal to 1, the SPS may further include a sps_extension_data_flag field.

The sps_extension_data_flag field may have any value.

The SPS according to embodiments may further include the neighbor pointselection-related option information. According to embodiments, theneighbor point selection-related option information may includeinformation related to NN_range.

According to embodiments, the neighbor point selection-related optioninformation may be included in an iteration statement repeated as manytimes as the value of the above-described sps_num_attribute_sets field.

That is, the iteration statement may further include anearest_neighbour_max_range[i] field and anearest_neighbour_min_range[i] field.

The nearest_neighbour_max_range[i] field may specify a maximum neighborpoint range applied when the i-th attribute of a corresponding sequenceis compressed. According to embodiments, the value of thenearest_neighbour_max_range[i] field may be used as the value ofNN_range of Equation 5. According to embodiments, thenearest_neighbour_max_range[i] field may be used to limit the distanceof a point registered as a neighbor. For example, if an LOD is generatedbased on an octree, the value of the nearest_neighbour_max_range[i]field may be the number of octree nodes around the point.

The nearest_neighbour_min_range[i] field may specify a minimum neighborpoint range when the i-th attribute of the sequence is compressed.

FIG. 34 shows an embodiment of a syntax structure of the geometryparameter set (GPS) (geometry_parameter_set( )) according to the presentdisclosure. The GPS according to the embodiments may contain informationon a method of encoding geometry information about point cloud datacontained in one or more slices.

According to embodiments, the GPS may include agps_geom_parameter_set_id field, a gps_seq_parameter_set_id field, agps_box_present_flag field, a unique_geometry_points_flag field, aneighbour_context_restriction_flag field, aninferred_direct_coding_mode_enabled_flag field, abitwise_occupancy_coding_flag field, anadjacent_child_contextualization_enabled_flag field, alog2_neighbour_avail_boundary field, a log2_intra_pred_max_node_sizefield, a log2_trisoup_node_size field, and a gps_extension_present_flagfield.

The gps_geom_parameter_set_id field provides an identifier for the GPSfor reference by other syntax elements.

The gps_seq_parameter_set_id field specifies the value ofsps_seq_parameter_set_id for the active SPS.

The gps_box_present_flag field specifies whether additional bounding boxinformation is provided in a geometry slice header that references thecurrent GPS. For example, the gps_box_present_flag field equal to 1 mayspecify that additional bounding box information is provided in ageometry header that references the current GPS. Accordingly, when thegps_box_present_flag field is equal to 1, the GPS may further include agps_gsh_box_log2_scale_present_flag field.

The gps_gsh_box_log2_scale_present_flag field specifies whether thegps_gsh_box_log2_scale field is signaled in each geometry slice headerthat references the current GPS. For example, thegps_gsh_box_log2_scale_present_flag field equal to 1 may specify thatthe gps_gsh_box_log2_scale field is signaled in each geometry sliceheader that references the current GPS. As another example, thegps_gsh_box_log2_scale_present_flag field equal to 0 may specify thatthe gps_gsh_box_log2_scale field is not signaled in each geometry sliceheader and a common scale for all slices is signaled in thegps_gsh_box_log2_scale field of the current GPS.

When the gps_gsh_box_log2_scale_present_flag field is equal to 0, theGPS may further include a gps_gsh_box_log2_scale field.

The gps_gsh_box_log2_scale field indicates the common scale factor ofthe bounding box origin for all slices that refer to the current GPS.

The unique_geometry_points_flag field indicates whether all outputpoints have unique positions. For example, theunique_geometry_points_flag field equal to 1 indicates that all outputpoints have unique positions. The unique_geometry_points_flag fieldequal to 0 indicates that in all slices that refer to the current GPS,the two or more of the output points may have the same position.

The neighbor_context_restriction_flag field indicates contexts used foroctree occupancy coding. For example, theneighbour_context_restriction_flag field equal to 0 indicates thatoctree occupancy coding uses contexts determined from six neighboringparent nodes. The neighbour_context_restriction_flag field equal to 1indicates that octree occupancy coding uses contexts determined fromsibling nodes only.

The inferred_direct_coding_mode_enabled_flag field indicates whether thedirect_mode_flag field is present in the geometry node syntax. Forexample, the inferred_direct_coding_mode_enabled_flag field equal to 1indicates that the direct_mode_flag field may be present in the geometrynode syntax. For example, the inferred_direct_coding_mode_enabled_flagfield equal to 0 indicates that the direct_mode_flag field is notpresent in the geometry node syntax.

The bitwise_occupancy_coding_flag field indicates whether geometry nodeoccupancy is encoded using bitwise contextualization of the syntaxelement occupancy map. For example, the bitwise_occupancy_coding_flagfield equal to 1 indicates that geometry node occupancy is encoded usingbitwise contextualisation of the syntax element occupancy map. Forexample, the bitwise_occupancy_coding_flag field equal to 0 indicatesthat geometry node occupancy is encoded using the dictionary encodedsyntax element occupancy_byte.

The adjacent_child_contextualization_enabled_flag field indicateswhether the adjacent children of neighboring octree nodes are used forbitwise occupancy contextualization. For example, theadjacent_child_contextualization_enabled_flag field equal to 1 indicatesthat the adjacent children of neighboring octree nodes are used forbitwise occupancy contextualization. For example,adjacent_child_contextualization_enabled_flag equal to 0 indicates thatthe children of neighbouring octree nodes are not used for the occupancycontextualization.

The log2_neighbour_avail_boundary field specifies the value of thevariable NeighbAvailBoundary that is used in the decoding process asfollows:NeighbAvailBoundary=2^(log2_neighbour_avail_boundary)

For example, when the neighbour_context_restriction_flag field is equalto 1, NeighbAvailabilityMask may be set equal to 1. For example, whenthe neighbour_context_restriction_flag field is equal to 0,NeighbAvailabilityMask may be set equal to1<<log2_neighbour_avail_boundary.

The log2_intra_pred_max_node_size field specifies the octree node sizeeligible for occupancy intra prediction.

The log2_trisoup_node_size field specifies the variable TrisoupNodeSizeas the size of the triangle nodes as follows.TrisoupNodeSize=1<<log2_trisoup_node_size

The gps_extension_present_flag field specifies whether thegps_extension_data syntax structure is present in the GPS syntaxstructure. For example, gps_extension_present_flag equal to 1 specifiesthat the gps_extension_data syntax structure is present in the GPSsyntax. For example, gps_extension_present_flag equal to 0 specifiesthat this syntax structure is not present in the GPS syntax.

When the value of the gps_extension_present_flag field is equal to 1,the GPS according to the embodiments may further include agps_extension_data_flag field.

The gps_extension_data_flag field may have any value. Its presence andvalue do not affect the decoder conformance to profiles.

FIG. 35 shows an embodiment of a syntax structure of the attributeparameter set (APS) (attribute_parameter_set( )) according to thepresent disclosure. The APS according to the embodiments may containinformation on a method of encoding attribute information in point clouddata contained in one or more slices. According to embodiments, the APSmay include neighbor point selection-related option information.

The APS according to the embodiments may include anaps_attr_parameter_set_id field, an aps_seq_parameter_set_id field, anattr_coding_type field, an aps_attr_initial_qp field, anaps_attr_chroma_qp_offset field, an aps_slice_qp_deltapresent_flagfield, and an aps_flag field.

The aps_attr_parameter_set_id field provides an identifier for the APSfor reference by other syntax elements.

The aps_seq_parameter_set_id field specifies the value ofsps_seq_parameter_set_id for the active SPS.

The attr_coding_type field indicates the coding type for the attribute.

In this example, the attr_coding_type field equal to 0 indicatespredicting weight lifting (or LOD with predicting transform) as thecoding type. The attr_coding_type field equal to 1 indicates RAHT as thecoding type. The attr_coding_type field equal to 2 indicates fix weightlifting (or LOD with lifting transform).

The aps_attr_initial_qp field specifies the initial value of thevariable SliceQp for each slice referring to the APS. The initial valueof SliceQp is modified at the attribute slice segment layer when anon-zero value of slice_qp_delta_luma or slice_qp_delta_luma are decoded

The aps_attr_chroma_qp_offset field specifies the offsets to the initialquantization parameter signaled by the syntax aps_attr_initial_qp.

The aps_slice_qp_delta_present_flag field specifies whether theash_attr_qp_delta_luma and ash_attr_qp_delta_chroma syntax elements arepresent in the attribute slice header (ASH). For example, theaps_slice_qp_delta_present_flag field equal to 1 specifies that theash_attr_qp_delta_luma and ash_attr_qp_delta_chroma syntax elements arepresent in the ASH. For example, the aps_slice_qp_delta_present_flagfield equal to 0 specifies that the ash_attr_qp_delta_luma andash_attr_qp_delta_chroma syntax elements are not present in the ASH.

When the value of the attr_coding_type field is 0 or 2, that is, thecoding type is predicting weight lifting (or LOD with predictingtransform) or fix weight lifting (or LOD with lifting transform), theAPS according to the embodiments may further include alifting_num_pred_nearest_neighbours field, alifting_max_num_direct_predictors

, a lifting_search_range field, alifting_lod_regular_sampling_enabled_flag field, alifting_num_detail_levels_minus1 field.

The lifting_num_pred_nearest_neighbours field specifies the maximumnumber (i.e., X value) of nearest neighbors to be used for prediction.

The lifting_max_num_direct_predictors field specifies the maximum numberof predictors to be used for direct prediction. The value of thevariable MaxNumPredictors that is used in the decoding process asfollows:MaxNumPredictors=lifting_max_num_direct_predictors field+1

The lifting_search_range field specifies a search range used todetermine nearest neighbors.

The lifting_num_detail_levels_minus1 field specifies the number oflevels of detail for the attribute coding.

The lifting_lod_regular_sampling_enabled_flag field specifies whetherlevels of detail (LOD) are built by using a regular sampling strategy.For example, the lifting_lod_regular_sampling_enabled_flag equal to 1specifies that levels of detail (LOD) are built by using a regularsampling strategy. The lifting_lod_regular_sampling_enabled_flag equalto 0 specifies that a distance-based sampling strategy is used instead.

The APS according to embodiments includes an iteration statementrepeated as many times as the value of thelifting_num_detail_levels_minus1 field. In an embodiment, the index(idx) is initialized to 0 and incremented by 1 every time the iterationstatement is executed, and the iteration statement is repeated until theindex (idx) is greater than the value of thelifting_num_detail_levels_minus1 field. This iteration statement mayinclude a lifting_sampling_period[idx] field when the value of thelifting_lod_decimation_enabled_flag field is true (e.g., 1), and mayinclude a lifting_sampling_distance_squared[idx] field when the value ofthe lifting_lod_decimation_enabled_flag field is false (e.g., 0).

The lifting_sampling_period[idx] field specifies the sampling period forthe level of detail idx.

The lifting_sampling_distance_squared[idx] field specifies the square ofthe sampling distance for the level of detail idx.

When the value of the attr_coding_type field is 0, that is, when thecoding type is predicting weight lifting (or LOD with predictingtransform), the APS according to the embodiments may further include alifting_adaptive_prediction_threshold field, and alifting_intra_lod_prediction_num_layers field.

The lifting_adaptive_prediction_threshold field specifies the thresholdto enable adaptive prediction.

The lifting_intra_lod_prediction_num_layers field specifies the numberof LOD layers where decoded points in the same LOD layer could bereferred to generate a prediction value of a target point. For example,the lifting_intra_lod_prediction_num_layers field equal tonum_detail_levels_minus1 plus 1 indicates that target point could referto decoded points in the same LOD layer for all LOD layers. For example,the lifting_intra_lod_prediction_num_layers field equal to 0 indicatesthat target point could not refer to decoded points in the same LoDlayer for any LoD layers.

The aps_extension_present_flag field specifies whether theaps_extension_data syntax structure is present in the APS syntaxstructure. For example, the aps_extension_present_flag field equal to 1specifies that the aps_extension_data syntax structure is present in theAPS syntax structure. For example, the aps_extension_present_flag fieldequal to 0 specifies that this syntax structure is not present in theAPS syntax structure.

When the value of the aps_extension_present_flag field is 1, the APSaccording to the embodiments may further include anaps_extension_data_flag_field.

The aps_extension_data_flag field may have any value. Its presence andvalue do not affect decoder conformance to profiles.

The APS according to embodiments may further include the neighbor pointselection-related option information.

When the value of the attr_coding_type field is 0 or 2, i.e., when acoding type is predicting weight lifting or LOD with predictingtransform, or fixed weight lifting or LOD with lifting transform, theAPS according to embodiments may further include anearest_neighbour_max_range field, a nearest_neighbour_min_range field,and a different_nn_range_in_tile_flag field.

The nearest_neighbour_max_range field may specify a maximum neighborpoint range. According to embodiments, the nearest_neighbour_max_rangefield may be used to limit the distance of a point registered as aneighbor. According to embodiments, the value of thenearest_neighbour_max_range field may be used as the value of NN_rangeof Equation 5. For example, if an LOD is generated based on an octree,the value of the nearest_neighbour_max_range field may be the number ofoctree nodes around the point.

The nearest_neighbour_min_range field may specify a minimum neighborpoint range when an attribute is compressed.

The different_nn_range_in_tile_flag field may specify whether acorresponding sequence uses a different maximum/minimum neighbor pointrange for a tile divided from the sequence.

According to embodiments, information about the maximum number that maybe set as a neighbor point (e.g., thelifting_num_pred_nearest_neighbours field) and information about asearch range (e.g., the lifting_search_range field) may also be includedin the neighbor point selection-related option information.

According to embodiments, the neighbor point selection-related optioninformation may further include at least one of information about an LODconfiguration method and/or information about a base neighbor pointdistance.

FIG. 36 illustrates another embodiment of a syntax structure of anattribute parameter set (APS) (attribute_parameter_set( )) according toembodiments. The APS according to embodiments may include informationabout a method of encoding attribute information of point cloud dataincluded in one or more slices. In particular, an example including theneighbor point selection-related option information is illustrated.

The syntax structure of the APS (attribute_parameter_set( )) of FIG. 36is the same as or similar to the syntax structure of the APS(attribute_parameter_set( )) of FIG. 35 except for the neighbor pointselection-related option information. Accordingly, for portions omittedor not described in the description of FIG. 36 , refer to FIG. 35 .

An aps_attr_parameter_set_id field specifies an ID of the APS forreference by other syntax elements.

An aps_seq_parameter_set_id field specifies the value ofsps_seq_parameter_set_id for an active SPS.

An attr_coding_type field represents a coding type for an attribute.

In an embodiment, if the value of the attr_coding_type field is 0, thecoding type may indicate predicting weight lifting or an LOD withpredicting transform and, if the value of the attr_coding_type field is1, the coding type may indicate RAHT. If the value of theattr_coding_type field is 2, the coding type may indicate fixed weightlifting or an LOD. with lifting transform.

When the value of the attr_coding_type field is 0 or 2, i.e., when thecoding type is predicting weight lifting or LOD with predictingtransform, or fixed weight lifting or LOD with lifting transform, theAPS according to embodiments may further include alifting_num_pred_nearest_neighbours field, alifting_max_num_direct_predictors field, a lifting_search_range field, alifting_lod_regular_sampling_enabled_flag field, and alifting_num_detail_levels_minus1 field,

The lifting_num_pred_nearest_neighbours field represents the maximumnumber of NNs (i.e., X value) to be used for prediction.

The lifting_max_num_direct_predictors field indicates the maximum numberof predictors to be used for direct prediction. The value of aMaxNumPredictors variable used in a point cloud data decoding processaccording to embodiments may be represented as follows.MaxNumPredictors=lifting_max_num_direct_predictors field+1

The lifting_search_range field indicates a search range used todetermine NNs.

The lifting_lod_regular_sampling_enabled_flag field indicates whether anLOD is built using a regular sampling strategy. For example, the valueof the lifting_lod_regular_sampling_enabled_flag field equal tospecifies that the LODs are built using the regular sampling strategy,and the value of the lifting_lod_regular_sampling_enabled_flag fieldequal to 0 specifies that a distance-based sampling strategy is usedinstead.

The lifting_num_detail_levels_minus1 field specifies the number of LODsfor attribute coding.

The APS according to embodiments includes an iteration statementrepeated as many times as the value of thelifting_num_detail_levels_minus1 field. In an embodiment, an index idxis initialized to 0 and increases by 1 each time the iteration statementis executed. The iteration statement is repeated until the index idx isgreater than the value of the lifting_num_detail_levels_minus1 field.This iteration statement may include a lifting_sampling_period[idx]field when the value of a lifting_lod_decimation_enabled_flag field istrue (e.g., 1) and may include a lifting_sampling_distance_squared[idx]field when the value of the lifting_lod_decimation_enabled_flag field isfalse (e.g., 0).

The lifting_sampling_period[idx] field represents a sampling period foran LOD idx.

The lifting_sampling_distance_squared[idx] field specifies the square ofa sampling distance for the LOD idx.

When the value of the attr_coding_type field is 0, i.e., when the codingtype is predicting weight lifting or an LOD with predicting transform,the APS according to embodiments may further include alifting_adaptive_prediction_threshold field and alifting_intra_lod_prediction_num_layers field,

The lifting_adaptive_prediction_threshold field specifies a threshold toenable adaptive prediction.

The lifting_intra_lod_prediction_num_layers field specifies the numberof LOD layers to which decoded points in the same LOD layer may refer togenerate a prediction value of a target point.

The APS according to embodiments may further include the neighbor pointselection-related option information.

When the value of the attr_coding_type field is 0 or 2, i.e., when thecoding type is predicting weight lifting or LOD with predictingtransform, or fixed weight lifting or LOD with lifting transform, theAPS according to embodiments may further include adifferent_nn_range_in_tile_flag field and adifferent_nn_range_per_lod_flag field.

The different_nn_range_in_tile_flag field may specify whether acorresponding sequence uses a different maximum/minimum neighbor pointrange for a tile divided from the sequence.

The different_nn_range_per_lod_flag field may specify whether to use adifferent maximum/minimum neighbor point range for each LOD.

For example, when the value of the different_nn_range_per_lod_flag fieldis false, the APS may further include a nearest_neighbour_max_rangefield and a nearest_neighbour_min_range field.

The nearest_neighbour_max_range field may specify a maximum neighborpoint range. According to embodiments, the nearest_neighbour_max_rangefield may be used to limit the distance of a point registered as aneighbor. According to embodiments, the value of thenearest_neighbour_max_range field may be used as the value of NN_rangeof Equation 5.

The nearest_neighbour_min_range field may specify a minimum neighborpoint range.

For example, if the value of the different_nn_range_per_lod_flag fieldis true, the APS further includes an iteration statement repeated asmany times as the value of the lifting_num_detail_levels_minus1 field.In an embodiment, an index idx is initialized to 0 and increases by 1each time the iteration statement is executed. The iteration statementis repeated until the index idx is greater than the value of thelifting_num_detail_levels_minus1 field. This iteration statement mayinclude a nearest_neighbour_max_range [idx] field and anearest_neighbour_min_range [idx] field.

The nearest_neighbour_max_range [idx] field may specify a maximumneighbor point range for an LOD idx. According to embodiments, thenearest_neighbour_max_range [idx] field may be used to limit thedistance of a point registered as a neighbor for the LOD idx. Accordingto embodiments, the value of the nearest_neighbour_max_range [idx] fieldmay be used as the value of NN_range for the LOD idx.

The nearest_neighbour_min_range [idx] field may indicate a minimumneighbor point range for the LOD idx.

According to embodiments, information about a maximum number that may beset as a neighbor point (e.g., the lifting_num_pred_nearest_neighboursfield) and information about a search range (e.g., thelifting_search_range field) may also be included in the neighbor pointselection-related option information.

According to embodiments, the neighbor point selection-related optioninformation may further include at least one of information about theLOD configuration method and/or information about the base neighborpoint distance.

FIG. 37 shows an embodiment of a syntax structure of a tile parameterset (TPS) (tile_parameter_set( )) according to the present disclosure.According to embodiments, a TPS may be referred to as a tile inventory.The TPS according to the embodiments includes information related toeach tile. In particular, in this example, the TPS includes neighborpoint selection-related option information.

The TPS according to the embodiments includes a num_tiles field.

The num_tiles field indicates the number of tiles signaled for thebitstream. When not present, num_tiles is inferred to be 0.

The TPS according to the embodiments includes an iteration statementrepeated as many times as the value of the num_tiles field. In anembodiment, i is initialized to 0, and is incremented by 1 each time theiteration statement is executed. The iteration statement is repeateduntil the value of i becomes equal to the value of the num_tiles field.The iteration statement may include a tile_bounding_box_offset_x[i]field, a tile_bounding_box_offset_y[i] field, atile_bounding_box_offset_z[i] field, a tile_bounding_box_size_width[i]field, a tile_bounding_box_size_height[i] field, and atile_bounding_box_size_depth[i] field.

The tile_bounding_box_offset_x[i] field indicates the x offset of thei-th tile in the Cartesian coordinates.

The tile_bounding_box_offset_y[i] field indicates the y offset of thei-th tile in the Cartesian coordinates.

The tile_bounding_box_offset_z[i] field indicates the z offset of thei-th tile in the Cartesian coordinates.

The tile_bounding_box_size_width[i] field indicates the width of thei-th tile in the Cartesian coordinates.

The tile_bounding_box_size_height[i] field indicates the height of thei-th tile in the Cartesian coordinates.

The tile_bounding_box_size_depth[i] field indicates the depth of thei-th tile in the Cartesian coordinates.

The TPS according to embodiments may further include the neighbor pointselection-related option information.

According to embodiments, the neighbor point selection-related optioninformation may be included in an iteration statement repeated as manytimes as the value of the num_tiles field as follows.

In an embodiment, if the value of the different_nn_range_in_tile_flagfield is true, the iteration statement may further include anearest_neighbour_max_range [i] field, a nearest_neighbour_min_range [i]field, and a different_nn_range_in_slice_flag [i] field. In anembodiment, the different_nn_range_in_tile_flag field is signaled in theAPS.

The nearest_neighbour_max_range [i] field may specify a maximum neighborpoint range of the i-th tile. According to embodiments, thenearest_neighbour_max_range [i] field may be used to limit the distanceof a point registered as a neighbor in the i-th tile. According toembodiments, the value of the nearest_neighbour_max_range [i] field maybe used as the value of NN_range of the i-th tile.

The nearest_neighbour_min_range field [i] may specify a minimum neighborpoint range of the i-th tile.

The different_nn_range_in_slice_flag [i] field may specify whether acorresponding tile uses a different maximum/minimum neighbor point rangefor a slice divided from the tile.

If the value of the different_nn_range_in_slice_flag [i] field is true,the TPS may further include anearest_neighbour_offset_range_in_slice_flag[i] field.

The nearest_neighbour_offset_range_in_slice_flag[i] field may indicatewhether the maximum/minimum neighbor point range defined in the slice ismarked as a range offset in the maximum/minimum neighbor point rangedefined in the tile or as an absolute value.

FIG. 38 shows an embodiment of a syntax structure of a geometry slicebitstream( ) according to the present disclosure.

The geometry slice bitstream (geometry_slice_bitstream( )) according tothe embodiments may include a geometry slice header(geometry_slice_header( )) and geometry slice data (geometry_slice_data()).

FIG. 39 shows an embodiment of a syntax structure of the geometry sliceheader (geometry_slice_header( )) according to the present disclosure.

A bitstream transmitted by the transmission device (or a bitstreamreceived by the reception device) according to the embodiments maycontain one or more slices. Each slice may include a geometry slice andan attribute slice. The geometry slice includes a geometry slice header(GSH). The attribute slice includes an attribute slice header (ASH).

The geometry slice header (geometry_slice_header( )) according toembodiments may include a gsh_geom_parameter_set_id field, a gsh_tile_idfield, a gsh_slice_id field, a gsh_max_node_size_log2 field, agsh_num_points field, and a byte_alignment( ) field.

When the value of the gps_box_present_flag field included in the GPS is‘true’ (e.g., 1), and the value of thegps_gsh_box_log2_scale_present_flag field is ‘true’ (e.g., 1), thegeometry slice header (geometry_slice_header( )) according to theembodiments may further include a gsh_box_log2 scale field, agsh_box_origin_x field, a gsh_box_origin_y field, and a gsh_box_origin_zfield.

The gsh_geom_parameter_set_id field specifies the value of thegps_geom_parameter_set_id of the active GPS.

The gsh_tile_id field specifies the value of the tile id that isreferred to by the GSH.

The gsh_slice_id specifies id of the slice for reference by other syntaxelements.

The gsh_box_log2 scale field specifies the scaling factor of thebounding box origin for the slice.

The gsh_box_origin_x field specifies the x value of the bounding boxorigin scaled by the value of the gsh_box_log2 scale field.

The gsh_box_origin_y field specifies the y value of the bounding boxorigin scaled by the value of the gsh_box_log2 scale field.

The gsh_box_origin_z field specifies the z value of the bounding boxorigin scaled by the value of the gsh_box_log2 scale field.

The gsh_max_node_size_log2 field specifies a size of a root geometryoctree node.

The gsh_points_number field specifies the number of coded points in theslice.

FIG. 40 shows an embodiment of a syntax structure of geometry slice data(geometry_slice_data( )) according to the present disclosure. Thegeometry slice data (geometry_slice_data( )) according to theembodiments may carry a geometry bitstream belonging to a correspondingslice.

The geometry_slice_data( ) according to the embodiments may include afirst iteration statement repeated as many times as by the value ofMaxGeometryOctreeDepth. In an embodiment, the depth is initialized to 0and is incremented by 1 each time the iteration statement is executed,and the first iteration statement is repeated until the depth becomesequal to MaxGeometryOctreeDepth. The first iteration statement mayinclude a second loop statement repeated as many times as the value ofNumNodesAtDepth. In an embodiment, nodeidx is initialized to 0 and isincremented by 1 each time the iteration statement is executed. Thesecond iteration statement is repeated until nodeidx becomes equal toNumNodesAtDepth. The second iteration statement may includexN=NodeX[depth][nodeIdx], yN=NodeY[depth][nodeIdx],zN=NodeZ[depth][nodeIdx], and geometry_node(depth, nodeIdx, xN, yN, zN).MaxGeometryOctreeDepth indicates the maximum value of the geometryoctree depth, and NumNodesAtDepth[depth] indicates the number of nodesto be decoded at the corresponding depth. The variablesNodeX[depth][nodeIdx], NodeY[depth][nodeIdx], and NodeZ[depth][nodeIdx]indicate the x, y, z coordinates of the nodeIdx-th node in decodingorder at a given depth. The geometry bitstream of the node of the depthis transmitted through geometry_node(depth, nodeIdx, xN, yN, zN).

The geometry slice data (geometry_slice_data( )) according to theembodiments may further include geometry_trisoup_data( ) when the valueof the log2_trisoup_node_size field is greater than 0. That is, when thesize of the triangle nodes is greater than 0, a geometry bitstreamsubjected to trisoup geometry encoding is transmitted throughgeometry_trisoup_data( ).

FIG. 41 shows an embodiment of a syntax structure ofattribute_slice_bitstream( ) according to the present disclosure.

The attribute slice bitstream (attribute_slice_bitstream( )) accordingto the embodiments may include an attribute slice header(attribute_slice_header( ) and attribute slice data(attribute_slice_data( )).

FIG. 42 shows an embodiment of a syntax structure of an attribute sliceheader (attribute_slice_header( )) according to the present disclosure.The attribute slice header according to the embodiments includessignaling information for a corresponding attribute slice. Inparticular, in this example, the attribute slice header includesneighbor point selection related option information.

The attribute slice header (attribute_slice_header( )) according to theembodiments may include an ash attrparameter_set_id field, anash_attr_sps_attr_idx field, and an ash_attr_geom_slice_id field.

When the value of the aps_slice_qp_deltapresent_flag field of the APS is‘true’ (e.g., 1), the attribute slice header (attribute_slice_header( ))according to the embodiments may further include an ash_qp_delta_lumafield and an ash_qp_delta_chroma field.

The ash_attr_parameter_set_id field specifies a value of theaps_attr_parameter_set_id field of the current active APS (e.g., theaps_attr_parameter_set_id field included in the APS described in FIG. 35or FIG. 36 ).

The ash_attr_sps_attr_idx field identifies an attribute set in thecurrent active SPS. The value of the ash_attr_sps_attr_idx field is inthe range from 0 to the sps_num_attribute_sets field included in thecurrent active SPS.

The ash_attr_geom_slice_id field specifies the value of the gsh_slice_idfield of the current geometry_slice_header.

The ash_qp_delta_luma field specifies a luma delta quantizationparameter (qp) derived from the initial slice qp in the active attributeparameter set.

The ash_qp_delta_chroma field specifies the chroma delta qp derived fromthe initial slice qp in the active attribute parameter set.

An attribute slice header (attribute_slice_header( )) according toembodiments may further include the neighbor point selection-relatedoption information as follows.

In an embodiment, if the value of the different_nn_range_in_slice_flagfield is true and the value of thenearest_neighbour_offset_range_in_slice_flag field is false, theattribute slice header may further include anearest_neighbour_absolute_max_range field and anearest_neighbour_absolute_min_range field.

The nearest_neighbour_absolute_max_range field may specify a maximumneighbor point range when an attribute of a corresponding slice iscompressed. According to embodiments, thenearest_neighbour_absolute_max_range field may be used to limit thedistance of a point registered as a neighbor in the slice. According toembodiments, the value of the nearest_neighbour_absolute_max_range fieldmay be used as the value of NN_range of the slice.

The nearest_neighbour_absolute_min_range field may indicate a minimumneighbor point range when an attribute of the slice is compressed.

In an embodiment, the different_nn_range_in_slice_flag field and thenearest_neighbour_offset_range_in_slice_flag field are signaled in theTPS.

In an embodiment, if the value of the different_nn_range_in_slice_flagfield is true and the value of thenearest_neighbour_offset_range_in_slice_flag field is true, theattribute slice header may further include a nearest_neighbour_max_rangeoffset field and a nearest_neighbour_min_range offset field.

The nearest_neighbour_max_range offset field may specify a maximumneighbor point range offset when an attribute of the slice iscompressed. In an embodiment, the reference of the neighbor pointmaximum range offset is a maximum neighbor point range of a tile towhich the slice belongs. According to embodiments, thenearest_neighbour_max_range offset field may be used to limit thedistance of a point registered as a neighbor in the slice. According toembodiments, the value of the nearest_neighbour_max_range offset fieldmay be used as an offset of the value of NN_range of the slice.

The nearest_neighbour_min_range offset field may indicate a minimumneighbor point range offset when an attribute of the slice iscompressed. In an embodiment, the reference of the minimum neighborpoint range offset is a minimum neighbor point range of a tile to whichthe slice belongs.

FIG. 43 illustrates another embodiment of a syntax structure of anattribute slice header (attribute_slice_header( )) according to thepresent disclosure. The attribute slice header according to embodimentsincludes signaling information for a corresponding attribute slice. Inparticular, an example including the neighbor point selection-relatedoption information is illustrated.

The syntax structure of the attribute slice header of FIG. 43 is thesame as or similar to the syntax structure of the attribute slice headerof FIG. 42 except for option information about neighbor point selection.Accordingly, for portions that are omitted or not described in thedescription of FIG. 43 , refer to FIG. 42 .

The ash_attr_parameter_set_id field represents the value of anaps_attr_parameter_set_id field of a current active APS (e.g., theaps_attr_parameter_set_id field included in the APS described withreference to FIG. 35 or FIG. 36 ).

The ash_attr_sps_attr_idx field identifies an attribute set in a currentactive SPS. The value of the ash_attr_sps_attr_idx field is in the rangefrom 0 to an sps_num_attribute_sets field included in the current activeSPS.

The ash_attr_geom_slice_id field specifies the value of a gsh_slice_idfield of a current geometry_slice_header.

The attribute slice header (attribute_slice_header( )) according toembodiments may further include the neighbor point selection-relatedoption information as follows.

In an embodiment, if the value of the different_nn_range_in_slice_flagfield is true, the attribute slice header may further include adifferent_nn_range_per_lod_flag field.

The different_nn_range_per_lod_flag field may specify whether todifferently use a maximum/minimum neighbor point range for each LOD.

For example, if the value of the different_nn_range_per_lod_flag fieldis false and the value of thenearest_neighbour_offset_range_in_slice_flag field is false, theattribute slice header may further include anearest_neighbour_absolute_max_range field and anearest_neighbour_absolute_min_range field.

The nearest_neighbour_absolute_max_range field may specify a maximumneighbor point range when an attribute of a corresponding slice iscompressed. According to embodiments, thenearest_neighbour_absolute_max_range field may be used to limit thedistance of a point registered as a neighbor in the slice. According toembodiments, the value of the nearest_neighbour_absolute_max_range fieldmay be used as the value of NN_range of the slice.

The nearest_neighbour_absolute_min_range field may specify a minimumneighbor point range when an attribute of the slice is compressed.

For example, if the value of the different_nn_range_per_lod_flag fieldis false and the value of thenearest_neighbour_offset_range_in_slice_flag field is true, theattribute slice header may further include a nearest_neighbour_max_rangeoffset field and a nearest_neighbour_min_range_offset field.

The nearest_neighbour_max_range_offset field may specify a maximumneighbor point range offset when an attribute of the slice iscompressed. In an embodiment, the reference of the maximum neighborpoint range offset is a maximum neighbor point range of a tile to whichthe slice belongs. According to embodiments, thenearest_neighbour_absolute_max_range field may be used to limit thedistance of a point registered as a neighbor in the slice. According toembodiments, the value of the nearest_neighbour_absolute_max_range fieldmay be used as an offset of the value of NN_range of the slice.

The nearest_neighbour_min_range_offset field may specify a minimumneighbor point range offset when an attribute of the slice iscompressed. In an embodiment, the reference of the minimum neighborpoint range offset is a minimum neighbor point range of a tile to whichthe slice belongs.

For example, if the value of the different_nn_range_per_lod_flag fieldis true and the value of thenearest_neighbour_offset_range_in_slice_flag field is false, theattribute slice header further includes an iteration statement repeatedas many times as the value of the lifting_num_detail_levels_minus1field. In an embodiment, an index (idx) is initialized to 0 andincreases by 1 each time the iteration statement is executed. Theiteration statement is repeated until the index idx is greater than thevalue of the lifting_num_detail_levels_minus1 field. This iterationstatement may include a nearest_neighbour_absolute_max_range[idx] fieldand a nearest_neighbour_absolute_min_range[idx] field.

In an embodiment, the lifting_num_detail_levels_minus1 field indicatesthe number of LODs for attribute coding and is signaled in the APS.

The nearest_neighbour_absolute_max_range[idx] field may indicate amaximum neighbor point range for an LOD idx when an attribute of theslice is compressed. According to embodiments, thenearest_neighbour_absolute_max_range [idx] field may be used to limitthe distance of a point registered as a neighbor for the LOD idx in theslice. According to embodiments, the value of thenearest_neighbour_absolute_max_range [idx] field may be used as thevalue of NN_range for the LOD idx of a corresponding slice.

The nearest_neighbour_absolute_min_range [idx] field may indicate aminimum neighbor point range for the LOD idx when an attribute of theslice is compressed.

For example, if the value of the different_nn_range_per_lod_flag fieldis true, and the value of thenearest_neighbour_offset_range_in_slice_flag field is true, theattribute slice header further includes an iteration statement repeatedas many times as the value of the lifting_num_detail_levels_minus1field. In an embodiment, the index idx is initialized to 0 and increasesby 1 each time the iteration statement is executed. The iterationstatement is repeated until the index idx is greater than the value ofthe lifting_num_detail_levels_minus1 field. This iteration statement mayinclude a nearest_neighbour_max_range offset [idx] field and anearest_neighbour_max_range offset [idx] field.

In an embodiment, the lifting_num_detail_levels_minus1 field indicatesthe number of LODs for attribute coding and is signaled in the APS.

The nearest_neighbour_max_range offset [idx] field may specify a maximumneighbor point range offset for the LOD idx when an attribute of theslice is compressed. According to an embodiment, the reference of themaximum neighbor point range offset is a maximum neighbor point range ofa tile to which the slice belongs. According to embodiments, thenearest_neighbour_max_range_offset [idx] field may be used to limit thedistance of a point registered as a neighbor for the LOD idx in theslice. According to embodiments, the value of thenearest_neighbour_max_range_offset [idx] field may be used as an offsetof the value of NN_range for the LOD idx of the slice.

The nearest_neighbour_min_range_offset [idx] field may specify a minimumneighbor point range offset for the LOD idx when an attribute of theslice is compressed. In an embodiment, the reference of the minimumneighbor point range offset is a minimum neighbor point range of a tileto which the slice belongs.

FIG. 44 shows an embodiment of a syntax structure of the attribute slicedata (attribute_slice_data( )) according to the present disclosure. Theattribute slice data (attribute_slice_data( )) according to theembodiments may carry an attribute bitstream belonging to acorresponding slice.

In the attribute slice data (attribute_slice_data( )) of FIG. 44 ,dimension=attribute_dimension[ash_attr_sps_attr_idx] represents theattribute dimension (attribute dimension) of the attribute setidentified by the ash_attr_sps_attr_idx field in the correspondingattribute slice header. The attribute dimension refers to the number ofcomponents constituting an attribute. An attribute according toembodiments represent reflectance, color, or the like. Accordingly, thenumber of components varies among attributes. For example, an attributecorresponding to color may have three color components (e.g., RGB).Accordingly, an attribute corresponding to reflectance may be amono-dimensional attribute, and the attribute corresponding to color maybe a three-dimensional attribute.

The attributes according to the embodiments may be attribute-encoded ona dimension-by-dimension basis.

For example, the attribute corresponding to reflectance and theattribute corresponding to color may be attribute-encoded, respectively.According to embodiments, attributes may be attribute-encoded togetherregardless of dimensions. For example, the attribute corresponding toreflectance and the attribute corresponding to color may beattribute-encoded together.

In FIG. 44 , zerorun specifies the number of 0 prior to residual).

In FIG. 44 , i denotes an i-th point value of the attribute. Accordingto an embodiment, the attr_coding_type field and thelifting_adaptive_prediction_threshold field are signaled in the APS.

MaxNumPredictors of FIG. 44 is a variable used in the point cloud datadecoding process, and may be acquired based on the value of thelifting_adaptive_prediction_threshold field signaled in the APS asfollows.MaxNumPredictors=lifting_max_num_direct_predictors field+1

Here, the lifting_max_num_direct_predictors field indicates the maximumnumber of predictors to be used for direct prediction.

According to the embodiments, predIndex[i] specifies the predictor index(or prediction mode) to decode the i-th point value of the attribute.The value of predIndex[i] is in the range from 0 to the value of thelifting_max_num_direct_predictors field.

The variable MaxPredDiff[i] according to the embodiments may becalculated as follows.

  minValue = maxValue = ã₀ for (j = 0; j < k; j++) { minValue =Min(minValue, ã_(j)) maxValue = Max(maxValue, ã_(j)) } MaxPredDiff[i] =maxValue − minValue;

Here, let k_(i) be the set of the k-nearest neighbors of the currentpoint i and let (ã_(j))_(j∈N) ₁ be their decoded/reconstructed attributevalues. The number of nearest neighbors, k_(i) shall be in the range of1 to lifting_num_pred_nearest_neighbours. According to embodiments, thedecoded/reconstructed attribute values of neighbors are derivedaccording to the Predictive Lifting decoding process.

The lifting_num_pred_nearest_neighbours field is signaled in the APS andindicates the maximum number of nearest neighbors to be used forprediction.

FIG. 45 is a flowchart of a method of transmitting point cloud dataaccording to embodiments.

The point cloud data transmission method according to the embodimentsmay include a step 71001 of encoding geometry contained in the pointcloud data, a step 71002 of encoding an attribute contained in the pointcloud data based on input and/or reconstructed geometry, and a step71003 of transmitting a bitstream including the encoded geometry, theencoded attribute, and signaling information.

The steps 71001 and 71002 of encoding the geometry and attributecontained in the point cloud data may perform some or all of theoperations of the point cloud video encoder 10002 of FIG. 1 , theencoding process 20001 of FIG. 2 , the point cloud video encoder of FIG.4 , the point cloud video encoder of FIG. 12 , the point cloud encodingprocess of FIG. 14 , the point cloud video encoder of FIG. 15 or thegeometry encoder and the attribute encoder of FIG. 17 .

In an embodiment, step 71002 of encoding the attribute may includegenerating an LOD_(l) set by applying at least one of an octree-basedLOD generation method, a distance-based LOD generation method, or asampling-based LOD generation method, searching for X (>0) NN points ina group having the same or lower LOD (i.e., a large distance betweennodes) based on the LOD_(l) set, and registering the X NN points as aneighbor point set in a predictor.

According to embodiments, step 71002 of encoding the attribute mayconfigure the neighbor point set by applying a search range and/or amaximum neighbor point distance.

According to embodiments, step 71002 of encoding the attribute mayobtain the maximum neighbor point distance by multiplying a baseneighbor point distance by NN_range. NN_range is a range within which aneighbor point may be selected and is referred to as a maximum neighborpoint range, a neighbor point range, or an NN_range.

The search range, the base neighbor point distance, and NN_range are thesame as those described with reference to FIGS. 15 to 27 and thus adetailed description thereof is omitted herein.

According to an embodiment, step 71002 of encoding the attributeincludes searching for X (e.g., 3) NN points among points within thesearch range in a group having the same or lower LOD (i.e., a largedistance between nodes) based on the LOD_(l) set as illustrated in FIG.26 . Then, only NN points within the maximum neighbor point distanceamong the X (e.g., 3) NN points may be registered as the neighbor pointset. Referring to FIG. 26 as an example, step 71002 of encoding theattribute includes comparing distance values between points within anactual search range and the point Px to search for X NN points andregistering only points within the maximum neighbor point distance at anLOD to which the point Px belongs among the X points as the neighborpoint set of the point Px. That is, the neighbor points registered asthe neighbor point set of the point Px are limited to points within themaximum neighbor point distance at the LOD to which the point Px belongsamong the X points.

According to another embodiment, step 71002 of encoding the attributemay include, as illustrated in FIG. 27 , searching for X (e.g., 3) NNpoints within the maximum neighbor point distance among points withinthe search range in a group having the same or lower LOD (i.e., a largedistance between nodes) based on the LOD_(l) set and registering the X(e.g., 3) NN points as the neighbor point set. Referring to FIG. 27 asan example, step 71002 of encoding the attribute includes comparing thedistance values between the points within the actual search range andthe point Px to search for X (e.g., 3) NN points within the maximumneighbor point distance at the LOD to which the point Px belongs fromamong points in the actual search range and registering the X NN pointsas the neighbor point set of the point Px. That is, the neighbor pointsregistered as the neighbor point set of the point Px are limited topoints within the maximum neighbor point distance at the LOD to whichthe point Px belongs.

According to embodiments, step 71002 of encoding the attribute includesacquiring a prediction attribute value of each point by applying one ofprediction modes 0 to 3 when one or more neighbor points are registeredin the predictor of each point and acquiring residual attribute valuesof points based on an original attribute value and the predicationattribute value of each point.

According to embodiments, prediction mode 0 is a mode that calculatesthe prediction attribute value through a weighted average, predictionmode 1 is a mode that determines an attribute of a first neighbor pointas the prediction attribute value, prediction mode 2 is a mode thatdetermines an attribute of a second neighbor point as the predictionattribute value, and prediction mode 3 is a mode that determines anattribute of a third neighbor point as the prediction attribute value.

According to embodiments, in step 71002 of encoding the attribute, if amaximum difference value between attribute values of neighbor pointsregistered in the predictor of a corresponding point is less than apreset threshold, prediction mode 0 is configured as a predication modeof the point. If the maximum difference value is equal to or greaterthan the preset threshold value, an RDO method is applied to a pluralityof candidate prediction modes and one of the candidate prediction modesis configured as the prediction mode of the point. In an embodiment,this process is performed upon each point.

According to embodiments, a prediction mode applied to each point may betransmitted in attribute slice data.

According to embodiments, the step 71002 may apply a quantization and azero-run length coding to the residual attribute values.

In the steps 71001 and 71002 according to embodiments, encoding may beperformed on the basis of a slice or a tile containing one or moreslices.

The step 71003 may be performed by the transmitter 10003 of FIG. 1 , thetransmitting process 20002 of FIG. 2 , transmission processor 12012 ofFIG. 12 or transmission processor 51008 of FIG. 15 .

FIG. 46 is a flowchart of a method of receiving point cloud dataaccording to embodiments.

According to embodiments, a point cloud data reception method mayinclude a step 81001 of receiving encoded geometries, encodedattributes, and signaling information, a step 81002 of decoding thegeometries based on the signaling information, a step 81003 of decodingthe attributes based on the signaling information and thedecoded/reconstructed geometries, and a step 81004 of rendering pointcloud data restored based on the decoded geometries and the decodedattributes.

The step 81001 according to embodiments may be performed by the receiver10005 of FIG. 1 , the transmitting process 20002 or the decoding process20003 of FIG. 2 , the receiver 13000 or the reception processor 13001 ofFIG. 13 or the reception processor 61001 of FIG. 20 .

In the steps 81002 and 81003 according to embodiments, decoding may beperformed on the basis of a slice or a tile containing one or moreslices.

According to embodiments, the step 81002 may perform some or all of theoperations of the point cloud video decoder 10006 of FIG. 1 , thedecoding process 20003 of FIG. 2 , the point cloud video decoder of FIG.11 , the point cloud video decoder of FIG. 13 , the geometry decoder ofFIG. 20 or the geometry decoder of FIGS. 21A and 21B.

According to embodiments, the step 81003 may perform some or all of theoperations of the point cloud video decoder 10006 of FIG. 1 , thedecoding process 20003 of FIG. 2 , the point cloud video decoder of FIG.11 , the point cloud video decoder of FIG. 13 , the attribute decoder ofFIG. 28 or the attribute decoder of FIG. 29 .

According to embodiments, signaling information, for example, at leastone of an SPS, an APS, a TPS, or an attribute slice header, may includeneighbor point selection-related option information. According toembodiments, the neighbor point selection-related option information mayinclude information about NN_range (e.g., nearest_neighbor_max_rangefield). According to embodiments, the neighbor point selection-relatedoption information may further include at least one of information aboutthe maximum number of points that may be set as neighbor points (e.g.,lifting_num_pred_nearest_neighbours field), information about a searchrange (e.g., lifting_search_range field), and information about an LODconfiguration method, or information about a base neighbor pointdistance.

In an embodiment, step 81003 of decoding the attribute may includegenerating an LOD_(l) set by applying at least one of an octree-basedLOD generation method, a distance-based LOD generation method, or asampling-based LOD generation method, searching for X (>0) NN points ina group having the same or lower LOD (i.e., a large distance betweennodes) based on the LOD_(l) set, and registering the X NN points as aneighbor point set in the predictor. In an embodiment, the LODgeneration method is signaled in signaling information (e.g., APS).

According to embodiments, step 81003 of decoding the attribute mayconfigure the neighbor point set by applying a search range and/or amaximum neighbor point distance.

According to embodiments, step 81003 of encoding the attribute mayobtain the maximum neighbor point distance by multiplying a baseneighbor point distance by NN_range. NN_range is a range in which aneighbor point may be selected and is referred to as a maximum neighborpoint range, a neighbor point range, or an NN_range.

The search range, the base neighbor point distance, and NN_range are thesame as those described with reference to FIGS. 15 to 29 and thus adetailed description thereof is omitted herein.

According to an embodiment, step 81003 of decoding the attributeincludes searching for X (e.g., 3) NN points among points within thesearch range in a group having the same or lower LOD (i.e., a largedistance between nodes) based on the LOD_(l) set as illustrated in FIG.26 . Then, only NN points within the maximum neighbor point distanceamong the X (e.g., 3) NN points may be registered as the neighbor pointset. Referring to FIG. 26 as an example, step 81003 of decoding theattribute includes comparing distance values between points within anactual search range and the point Px to search for X NN points andregistering only points within the maximum neighbor point distance at anLOD to which the point Px belongs among the X points as the neighborpoint set of the point Px. That is, the neighbor points registered asthe neighbor point set of the point Px are limited to points within themaximum neighbor point distance at the LOD to which the point Px belongsamong the X points.

According to another embodiment, step 81003 of decoding the attributemay include, as illustrated in FIG. 27 , searching for X (e.g., 3) NNpoints within the maximum neighbor point distance among points withinthe search range in a group having the same or lower LOD (i.e., a largedistance between nodes) based on the LOD_(l) set and registering the X(e.g., 3) NN points as the neighbor point set. Referring to FIG. 27 asan example, step 81003 of decoding the attribute includes comparing thedistance values between the points within the actual search range andthe point Px to search for X (e.g., 3) NN points within the maximumneighbor point distance at the LOD to which the point Px belongs frompoints in the actual search range and registering the X NN points as theneighbor point set of the point Px. That is, neighbor points registeredas the neighbor point set of the point Px are limited to points withinthe maximum neighbor point distance at the LOD to which the point Pxbelongs.

According to embodiments, step 81003 of decoding the attribute mayinclude acquiring a prediction attribute value of a corresponding pointby applying one of prediction modes 0 to 3 when one or more neighborpoints are registered in the predictor of a specific point.

According to embodiments, prediction mode 0 is a mode that calculates aprediction attribute value through a weighted average, prediction mode 1is a mode that determines an attribute of a first neighbor point as theprediction attribute value, prediction mode 2 is a mode that determinesan attribute of a second neighbor point as the prediction attributevalue, and prediction mode 3 is a mode that determines an attribute of athird neighbor point as the prediction attribute value.

According to embodiments, a prediction mode of a specific point may beconfigured as a default and predictor index information (predIndex)indicating the prediction mode may be signaled in attribute slice data.

According to an embodiment, the step 81003 may predict an attributevalue of a point to be decoded based on the prediction mode 0 when thepredictor index information of the point is not signaled in theattribute slice data.

According to an embodiment, the step 81003 may predict an attributevalue of a point to be decoded based on a prediction mode that issignaled in the attribute slice data when the predictor indexinformation of the point is signaled in the attribute slice data.

According to an embodiment, the step 81003 may restore an attributevalue of the point by adding the predicted attribute value of the pointand the received residual attribute value of the point. In anembodiment, an attribute value of each point may be restored byperforming this process for each point.

According to an embodiment, the step 81003 may perform a zero-run lengthdecoding on the received residual attribute values, which is an inverseprocess of a zero-run length encoding of a transmitting side, prior torestore the attribute values when the received residual attribute valuesare zero-run length encoded.

In the step 81004 of rendering the point cloud data according to theembodiments, the point cloud data may be rendered according to variousrendering methods. For example, the points of the point cloud contentmay be rendered onto a vertex having a certain thickness, a cube of aspecific minimum size centered on the vertex position, or a circlecentered on the vertex position. All or part of the rendered point cloudcontent is provided to the user through a display (e.g. a VR/AR display,a general display, etc.).

The step 81004 according to the embodiments may be performed by therenderer 10007 of FIG. 1 , the rendering process 20004 of FIG. 2 , orthe renderer 13011 of FIG. 13 .

As described above, according to the present disclosure, selection ofmeaningless neighbor points is excluded and meaningful neighbor pointsmay be selected when configuring a neighbor point set by considering anattribute correlation between points of content during attributeencoding of point cloud content. Then, since a residual attribute valueis reduced and a bitstream size is thereby reduced, compressionefficiency of attributes is improved. In other words, in the presentdisclosure, compression efficiency of attributes may be improved using amethod of restricting points that may be selected as the neighbor pointset in consideration of the attribute correlation between points ofcontent.

A point cloud data transmission method, a point cloud data transmissiondevice, a point cloud data reception method, and a point cloud datareception device according to embodiments may provide a good-qualitypoint cloud service.

A point cloud data transmission method, a point cloud data transmissiondevice, a point cloud data reception method, and a point cloud datareception device according to embodiments may achieve various videocodec methods.

A point cloud data transmission method, a point cloud data transmissiondevice, a point cloud data reception method, and a point cloud datareception device according to embodiments may provide universal pointcloud content such as a self-driving service (or an autonomous drivingservice).

A point cloud data transmission method, a point cloud data transmissiondevice, a point cloud data reception method, and a point cloud datareception device according to embodiments may perform space-adaptivepartition of point cloud data for independent encoding and decoding ofthe point cloud data, thereby improving parallel processing andproviding scalability.

A point cloud data transmission method, a point cloud data transmissiondevice, a point cloud data reception method, and a point cloud datareception device according to embodiments may perform encoding anddecoding by spatially partitioning the point cloud data in units oftiles and/or slices, and signal necessary data therefore, therebyimproving encoding and decoding performance of the point cloud.

A point cloud data transmission method and apparatus, and a point clouddata reception method and apparatus according to embodiments reduce thesize of an attribute bitstream and enhance compression efficiency ofattributes by selecting neighbor points used for attribute prediction inconsideration of an attribute correlation between points of content whenattribute information of G-PCC is encoded.

A point cloud data transmission method and apparatus, and a point clouddata reception method and apparatus according to embodiments reduce thesize of an attribute bitstream and enhance compression efficiency ofattributes by applying a maximum neighbor point distance to selectneighbor points used for attribute prediction when attribute informationof G-PCC is encoded.

Each part, module, or unit described above may be a software, processor,or hardware part that executes successive procedures stored in a memory(or storage unit). Each of the steps described in the above embodimentsmay be performed by a processor, software, or hardware parts. Eachmodule/block/unit described in the above embodiments may operate as aprocessor, software, or hardware. In addition, the methods presented bythe embodiments may be executed as code. This code may be written on aprocessor readable storage medium and thus read by a processor providedby an apparatus.

In the specification, when a part “comprises” or “includes” an element,it means that the part further comprises or includes another elementunless otherwise mentioned. Also, the term “ . . . module (or unit)”disclosed in the specification means a unit for processing at least onefunction or operation, and may be implemented by hardware, software orcombination of hardware and software.

Although embodiments have been explained with reference to each of theaccompanying drawings for simplicity, it is possible to design newembodiments by merging the embodiments illustrated in the accompanyingdrawings. If a recording medium readable by a computer, in whichprograms for executing the embodiments mentioned in the foregoingdescription are recorded, is designed by those skilled in the art, itmay fall within the scope of the appended claims and their equivalents.

The apparatuses and methods may not be limited by the configurations andmethods of the embodiments described above. The embodiments describedabove may be configured by being selectively combined with one anotherentirely or in part to enable various modifications.

Although preferred embodiments have been described with reference to thedrawings, those skilled in the art will appreciate that variousmodifications and variations may be made in the embodiments withoutdeparting from the spirit or scope of the disclosure described in theappended claims. Such modifications are not to be understoodindividually from the technical idea or perspective of the embodiments.

Various elements of the apparatuses of the embodiments may beimplemented by hardware, software, firmware, or a combination thereof.Various elements in the embodiments may be implemented by a single chip,for example, a single hardware circuit. According to embodiments, thecomponents according to the embodiments may be implemented as separatechips, respectively. According to embodiments, at least one or more ofthe components of the apparatus according to the embodiments may includeone or more processors capable of executing one or more programs. Theone or more programs may perform any one or more of theoperations/methods according to the embodiments or include instructionsfor performing the same. Executable instructions for performing themethod/operations of the apparatus according to the embodiments may bestored in a non-transitory CRM or other computer program productsconfigured to be executed by one or more processors, or may be stored ina transitory CRM or other computer program products configured to beexecuted by one or more processors. In addition, the memory according tothe embodiments may be used as a concept covering not only volatilememories (e.g., RAM) but also nonvolatile memories, flash memories, andPROMs. In addition, it may also be implemented in the form of a carrierwave, such as transmission over the Internet. In addition, theprocessor-readable recording medium may be distributed to computersystems connected over a network such that the processor-readable codemay be stored and executed in a distributed fashion.

In this document, the term “I” and “,” should be interpreted asindicating “and/or.” For instance, the expression “A/B” may mean “Aand/or B.” Further, “A, B” may mean “A and/or B.” Further, “AB/C” maymean “at least one of A, B, and/or C.” “A, B, C” may also mean “at leastone of A, B, and/or C.” Further, in the document, the term “or” shouldbe interpreted as “and/or.” For instance, the expression “A or B” maymean 1) only A, 2) only B, and/or 3) both A and B. In other words, theterm “or” in this document should be interpreted as “additionally oralternatively.”

Terms such as first and second may be used to describe various elementsof the embodiments. However, various components according to theembodiments should not be limited by the above terms. These terms areonly used to distinguish one element from another. For example, a firstuser input signal may be referred to as a second user input signal.Similarly, the second user input signal may be referred to as a firstuser input signal. Use of these terms should be construed as notdeparting from the scope of the various embodiments. The first userinput signal and the second user input signal are both user inputsignals, but do not mean the same user input signal unless contextclearly dictates otherwise. The terminology used to describe theembodiments is used for the purpose of describing particular embodimentsonly and is not intended to be limiting of the embodiments. As used inthe description of the embodiments and in the claims, the singular forms“a”, “an”, and “the” include plural referents unless the context clearlydictates otherwise. The expression “and/or” is used to include allpossible combinations of terms. The terms such as “includes” or “has”are intended to indicate existence of figures, numbers, steps, elements,and/or components and should be understood as not precluding possibilityof existence of additional existence of figures, numbers, steps,elements, and/or components.

As used herein, conditional expressions such as “if” and “when” are notlimited to an optional case and are intended to be interpreted, when aspecific condition is satisfied, to perform the related operation orinterpret the related definition according to the specific condition.Embodiments may include variations/modifications within the scope of theclaims and their equivalents. It will be apparent to those skilled inthe art that various modifications and variations can be made in thepresent invention without departing from the spirit and scope of theinvention. Thus, it is intended that the present invention cover themodifications and variations of this invention provided they come withinthe scope of the appended claims and their equivalents.

Furthermore, operations according to embodiments describing in thepresent document may be performed by transmitting/receiving devicesincluding a memory and/or a processor. The memory may store programs toprocess/control operations according to embodiments and the processormay control variable operations describing in the present document. Theprocessor may be called a controller, etc. Operations according toembodiments may be implemented by firmware, software, and/or acombination thereof and the firmware, software, and/or a combinationthereof may be stored in a processor or a memory.

What is claimed is:
 1. A point cloud data transmission method, themethod comprising: acquiring point cloud data; encoding geometryinformation including positions of points of the point cloud data;generating one or more levels of detail (LODs) based on the geometryinformation and selecting one or more nearest neighbor points of eachpoint to be attribute-encoded based on the one or more LODs, wherein theselected one or more nearest neighbor points of each point are locatedwithin a maximum nearest neighbor point distance, and wherein themaximum nearest neighbor point distance is obtained by multiplying abase distance by a maximum nearest neighbor point range; encodingattribute information of each point based on the selected one or morenearest neighbor points of each point; and transmitting the encodedgeometry information, the encoded attribute information, and signalinginformation, wherein the signaling information includes an attributeparameter set and wherein the attribute parameter set includesinformation related to the maximum nearest neighbor point range.
 2. Themethod of claim 1, wherein the maximum nearest neighbor point range is anumber of nodes of each point.
 3. The method of claim 1, wherein, whenthe one or more LODs are generated based on an octree, the base distanceis determined based on a diagonal distance of one node at a specificLOD.
 4. A point cloud data transmission apparatus, the apparatuscomprising: an acquisition unit configured to acquire point cloud data;a geometry encoder configured to encode geometry information includingpositions of points of the point cloud data; an attribute encoderconfigured to generate one or more levels of detail (LODs) based on thegeometry information, select one or more nearest neighbor points of eachpoint to be attribute-encoded based on the one or more LODs, and encodeattribute information of each point based on the selected one or morenearest neighbor points of each point, wherein the selected one or morenearest neighbor points of each point are located within a maximumnearest neighbor point distance, and wherein the maximum nearestneighbor point distance is obtained by multiplying a base distance by amaximum nearest neighbor point range; and a transmitter configured totransmit the encoded geometry information, the encoded attributeinformation, and signaling information, wherein the signalinginformation includes an attribute parameter set and wherein theattribute parameter set includes information related to the maximumnearest neighbor point range.
 5. The apparatus of claim 4, wherein themaximum nearest neighbor point range is a number of nodes of each point.6. The apparatus of claim 4, wherein, when the one or more LODs aregenerated based on an octree, the base distance is determined based on adiagonal distance of one node at a specific LOD.
 7. A point cloud datareception apparatus, the apparatus comprising: a receiver configured toreceive geometry information, attribute information, and signalinginformation; a geometry decoder configured to decode the geometryinformation based on the signaling information; an attribute decoderconfigured to decode the attribute information of each point based onone or more nearest neighbor points of each point and the signalinginformation, wherein the one or more nearest neighbor points of eachpoint to be attribute decoded are selected based on one or more levelsof detail (LODs), wherein the one or more LODs are generated based onthe geometry information, wherein the selected one or more nearestneighbor points of each point are located within a maximum nearestneighbor point distance, wherein the maximum nearest neighbor pointdistance is obtained by multiplying a base distance by a maximum nearestneighbor point range, wherein the signaling information includes anattribute parameter set, and wherein the attribute parameter setincludes information related to the maximum nearest neighbor pointrange; and a renderer configured to render point cloud data restoredbased on the decoded geometry information and the decoded attributeinformation.
 8. The apparatus of claim 7, wherein the maximum nearestneighbor point range is a number of nodes of each point.
 9. Theapparatus of claim 7, wherein, when the one or more LODs are generatedbased on an octree, the base distance is determined based on a diagonaldistance of one node at a specific LOD.
 10. A point cloud data receptionmethod, the method comprising: receiving geometry information, attributeinformation, and signaling information; decoding the geometryinformation based on the signaling information; decoding the attributeinformation of each point based on one or more nearest neighbor pointsof each point and the signaling information, wherein the one or morenearest neighbor points of each point to be attribute decoded areselected based on one or more levels of detail (LODs), wherein the oneor more LODs are generated based on the geometry information, whereinthe selected one or more nearest neighbor points of each point arelocated within a maximum nearest neighbor point distance, and whereinthe maximum nearest neighbor point distance is obtained by multiplying abase distance by a maximum nearest neighbor point range, wherein thesignaling information includes an attribute parameter set, and whereinthe attribute parameter set includes information related to the maximumnearest neighbor point range; and rendering point cloud data restoredbased on the decoded geometry information and the decoded attributeinformation.
 11. The method of claim 10, wherein the maximum nearestneighbor point range is a number of nodes of each point.
 12. The methodof claim 10, wherein, when the one or more LODs are generated based onan octree, the base distance is determined based on a diagonal distanceof one node at a specific LOD.